The Swiggy Data Scientist interview guide, interview questions, salary data, and interview experiences.

Swiggy Data Scientist Interview Guide

<p>Getting ready for an Data Scientist interview at Swiggy? The Swiggy Data Scientist interview span across 10 to 12 different question topics. In preparing for the interview:</p>
<ul>
<li>Know what skills are necessary for Swiggy Data Scientist roles.</li>
<li>Gain insights into the Data Scientist interview process at Swiggy.</li>
<li>Practice real Swiggy Data Scientist interview questions.</li>
</ul>
<p>Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Swiggy Data Scientist interview.</p>


Getting ready for an Data Scientist interview at Swiggy? The Swiggy Data Scientist interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Swiggy Data Scientist roles.
- Gain insights into the Data Scientist interview process at Swiggy.
- Practice real Swiggy Data Scientist interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Swiggy Data Scientist interview.
        

Overview

Challenge

<p>We've gathered this data from parsing thousands of interview experiences sourced from members.</p>

We've gathered this data from parsing thousands of interview experiences sourced from members.

Swiggy Data Scientist Interview Process

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Closest SAT Scores

Subscription Overlap

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Compute Deviation

Get Top N Frequent Words

String Shift

Weekly Aggregation

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

RMS Error

A/B Testing

Experiment Validity

How do we go about selecting the best 10,000 customers for the pre-launch?

Pre-Launching Shows

Delivery Estimate Model

Button AB Test

A/B Test Power Size

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Jars and Coins

<p>When an interviewer asks a question along the lines of:</p>

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

<p>How would you respond?</p>


<p>When asked about your strengths in an interview, what is an effective way to respond?</p>

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

<p>Which of the following is an acceptable strategy when discussing weaknesses in an interview?</p>

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Brainteasers

<p>When an interviewer asks you a question along the lines of:</p>

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

<p>How should you respond?</p>


<p>When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?</p>

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

<p>How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'</p>

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

<p>When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?</p>

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

<p>Describe a data project you worked on. What were some of the challenges you faced?</p>


Describing a data project and its challenges

Hurdles In Data Projects

Analytics

<p>How would you explain what a p-value is to someone who is not technical?</p>


<p>What does a p-value in a statistical test represent?</p>

What does a p-value in a statistical test represent?

P-value to a Layman I

<p>In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?</p>

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

P-value to a Layman

Statistics

<p>What are the assumptions of linear regression?</p>


Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

<p>How would you handle the data preparation for building a machine learning model using imbalanced data?</p>


Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

<p>Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? </p>

<p>Give an example of the tradeoffs between the two.</p>


<p>In machine learning, when would you use a bagging algorithm over a boosting algorithm?</p>


In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Bagging vs Boosting

<p>Write a SQL query to select the 2nd highest salary in the engineering department.</p>

<p><strong>Note:</strong> If more than one person shares the highest salary, the query should select the next highest salary.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


<p>Let’s say you work at Allstate. Allstate is running <code>N</code>  online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).</p>

<p>Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations</p>

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

<p><em>Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types</em></p>

<p><em>Note: Please return only one query with each number in a different row</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>join_type</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_rows</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Size of Joins

<p>Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.</p>

<p>How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?</p>


Bias vs. Variance Tradeoff

<p>Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.</p>


<p>Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?</p>


Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Xgboost vs Random Forest

<p>How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?</p>


<p>What happens when you average the output on multiple decision trees?</p>


What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

<p>What is the difference between Logistic and Linear Regression?</p>

<p>When would use one instead of the other in practice?</p>


What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

<p>Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.</p>

<p>Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.</p>

<p>How would you approach designing this test?  How would you determine whether the increase in pricing is a good business decision?</p>


Testing Price Increase

<p>Tell me about a project in which you had to clean and organize a large dataset.</p>


Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Data Pipelines

<p>A team wants to A/B test multiple different changes through a sign-up funnel.</p>

<p>For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.</p>

<p>How would you set up this test?</p>


<p>Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.</p>

<p>How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.</p>


Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

<p>Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?</p>


<p>How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?</p>


How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

<p>How would you choose the <em>k</em> value when using <em>k</em>-means clustering?</p>


choosing k value during k-means clustering

Choosing k

<p>Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.</p>

<p><em>Note: Can you do it on $O(n)$ time?</em></p>

<p><em>Note: Even though there could be many solutions, only one needs to be returned.</em></p>

<p><strong>Example 1:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">array</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span><span class="p">]</span> 
</span></span><span class="line"><span class="cl"><span class="n">target</span> <span class="o">=</span> <span class="mi">5</span> 
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">sum_pair_indices</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">3</span><span class="p">]</span> <span class="ow">or</span> <span class="p">[</span><span class="mi">1</span> <span class="mi">2</span><span class="p">]</span>
</span></span></code></pre>

<p><strong>Example 2:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">array</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">target</span> <span class="o">=</span> <span class="mi">6</span> 
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">Do</span> <span class="n">NOT</span> <span class="k">return</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">0</span><span class="p">]</span> <span class="k">as</span> <span class="n">you</span> <span class="n">can</span><span class="s1">&#39;t use an index twice.</span>
</span></span></code></pre>


Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

<p>Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.</p>

<p>How would you assess the validity of the result?</p>


<p>Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>How would we build this model?</p>


<p>Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>Which statement is true?</p>


Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Bank Fraud Model

<p>Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.</p>

<p><em>Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">N</span> <span class="o">=</span> <span class="mi">3</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">prime_numbers</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span></code></pre>


Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

<p>Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.</p>

<p>When should you use one versus the other?</p>


<p>Given the choices below, which one is an example of a scenario where we should be using regularization?</p>


Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

<p>What’s the difference between Lasso and Ridge Regression?</p>


<p>Under what circumstances is it recommended to use Lasso regression over Ridge regression?</p>

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Lasso vs Ridge

<h1>Binary Tree Validation</h1>

<p>You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).</p>

<p>A valid BST is defined as follows:</p>

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

<p>Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.</p>

<h3>Example:</h3>

<p><strong>Input:</strong></p>

<p><img src="/s3/questions/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png</p>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">is_valid_bst</span><span class="p">(</span><span class="n">Node</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>


Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

<p>How would you convey insights and the methods you use to a non-technical audience?</p>


Making data-driven insights actionable for those without technical expertise

Simple Explanations

<p>Let’s say you are working on Google Docs. A product manager comes to you and asks how the product is doing. </p>

<p>What are the top five metrics that you would start tracking to understand the health of Google Docs?</p>


Docs Metrics

Product Sense & Metrics

<p>What’s the relationship between PCA and K-means clustering?</p>


<p>What does the variable “k” in k-means clustering refer to?</p>


What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

<p>Let’s say you’re a data engineer at Fidelity Investments, and you’re running a SQL query on a cloud-based data warehouse. All cluster resources and network health metrics look normal, but the query is still taking over 10 minutes to complete.</p>

<p>How would you go about diagnosing and improving the performance of this query?</p>


How would you diagnose and speed up a slow SQL query when system metrics look healthy?

Slow SQL Query

Query Optimization

<p>Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). </p>

<p><em>Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order.</em> </p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>employees</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong>:</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Get the top 3 highest employee salaries by department

Top Three Salaries

Swiggy Data Scientist Interview Questions

<h5>Senior Staff Data Scientist </h5><p>This is a hybrid role based in our Bay Area offices and will require you to be in office Tuesdays and Thursdays. </p><p>What's so interesting about this role? </p><p>Grindr is the largest LGBTQ+ Social App in the world, used by over 14M+ monthly users in nearly every country in the world. We are a fundamental part of the global LGBTQ+ community and are a pillar in gay culture. </p><p>As a Senior Staff Data Scientist at Grindr, you will lead cross-functional collaboration with product managers, product designers, and engineers to devise insightful metrics that guide development, shape and oversee novel experiments, present a data-driven viewpoint to guide key decisions, and identify and drive new strategic avenues for growth through deep dive analysis. You will have the opportunity to work on models that are deployed to production and improve quality of life for our millions of users. You will also be an informal ambassador for the Data Science team to the rest of the organization, educating a diverse group on the many subtle aspects of utilizing data well. </p><p>You will be a member of the data organization at Grindr, which integrates data scientists, data engineers, and ML/AI engineers into a single cohesive and collaborative team. This is a rare opportunity to learn, teach, and have an outsized impact alongside some of the top minds in the field. </p><p>What's the job? </p><ol> <li>Extract actionable insights from broad, open-ended questions </li><li>Set strategy for designing and evaluating experiments to measure the impact of product changes </li><li>Influence and communicate results to executives and cross-functional stakeholders to inform product and company decisions </li><li>Analyze data from across the product to uncover the root causes of metric movements </li><li>Communicate results to cross-functional stakeholders to inform product decisions </li><li>Architect and guide development of tools to scale and automate analyses, improving productivity across the company </li><li>Provide mentorship and guidance to team members + identify and recommend best practices </li><li>Apply an engineering mindset: minimize complexity while maximizing utility & maintainability </li><li>Shape the future state of our data products, devising ML solutions to improve recommendations, detect spam, and better serve our users </li></ol> <p>What we'll love about you </p><ul> <li>BS or higher in Computer Science, Statistics, Mathematics, Physics, a related quantitative field, or equivalent experience </li><li>10+ years of industry data science experience, with a track record of influencing senior stakeholders and company-level outcomes </li><li>Experience effectively communicating & collaborating with non-technical stakeholders </li><li>A deep understanding of the "why" behind your work, not just the how </li><li>Strong Python skills in data analysis using pandas and Jupyter Notebook </li><li>Experience writing and optimizing complex SQL queries on large data sets </li><li>A strong background in inferential statistics, especially in an experimentation setting (hypothesis testing, power analysis, experimental design) </li><li>Experience with different data analytics and BI tools such as Looker, Tableau, PowerBI, or Snowflake </li><li>Experience in big data technologies like Spark </li></ul> <p>We'll Really Swoon If You Have </p><ul> <li>A machine learning background with an understanding of when ML is the right tool and the subtle science of creating models that function well in the real world would be a nice to have </li><li>You have excellent communication, presentation, and documentation skills with the ability to define and communicate the "big picture" to key stakeholders at various levels and technical understanding </li><li>Experience with Statsig would be nice-to-have </li></ul> <p>What you'll love about us </p><ul> <li>Mission and Impact: Grindr is building the global gayborhood in your pocket. Your role will impact the lives of millions of LGBTQ+ people around the world. Through our success, we are making a world where the lives of our community are free, equal, and just. </li><li>Family Insurance: Insurance premium coverage for health, dental, and vision for you and partial coverage for your dependents. </li><li>Retirement Savings: Generous 401K plan with 6% match and immediate vest in the U.S. </li><li>Compensation: Industry-competitive compensation and eligibility for company bonus and equity programs. </li><li>Queer-Inclusive Benefits: Industry-leading gender-affirming offerings with up to 90% cost coverage, access to Included Health, monthly stipends for HRT, and more. </li><li>Additional Benefits: Flexible vacation policy, monthly stipends for cell phone, internet, wellness, food, and commuting, breakfast/lunch provided onsite, and yearly travel & leisure stipend. </li></ul> <p>About Grindr </p><p>Grindr is building the global gayborhood in your pocket. With more than 14.5 million monthly active users, Grindr has become a fundamental part of the LGBTQ+ community and is charting a path to make the world more free, equal, and just. Since 2015, Grindr for Equality has advanced safety, health, and human rights for millions of Grindr users and the global LGBTQ+ community in partnership with more than 100 community organizations in every region of the world. </p><p>Our next evolution is underway as a public company that continues to grow and build meaningful experiences for our users. From social issues to product innovations, we're setting audacious goals for our community and the business, and leveraging the latest tech stacks and a culture of engineering excellence to make it happen. At the heart of our work in this new chapter is a shared set of operating principles centered around cultivating curiosity, thinking big, setting and expediting our ambitious goals, and growing through iteration; all while keeping our users #1. </p><p>Grindr is headquartered in West Hollywood, California, with offices in the Bay Area, Chicago, and New York. With a track record of strong financial performance and plans for continued headcount growth, we're building a team of talented, passionate, and open-minded people who want to disrupt the dating app space, innovate products, and advance LGBTQ+ culture. Come be a part of this exciting journey with us. </p><p>Grindr is an equal-opportunity employer. </p>

Principal Data Scientist, AI Foundations

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description
AI Foundations Specialist Models Data Science team builds and ships state of the art scalable architecture, AI/ML solutions for Capital One's award-winning mobile app. We partner with product, tech and design teams to deliver app features that delight customers with dynamic and personalized experiences, enable them to chat with Capital One's digital assistant Eno, or search for useful contents. You will be the driving force to experiment, innovate and create next generation experiences powered by the latest emerging generative AI technologies.

In this role, you will:

* Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
* Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
* Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
* Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
* Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

The Ideal Candidate is:

* Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
* Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
* Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.
* A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
* Technical. You're comfortable with advanced ML and DL technologies including language models and are passionate about developing further. You have hands-on experience working with LLMs and solutions using open-source tools and cloud computing platforms.
* Influential. You are passionate about AI/ML and can bring along a cross functional team in breakthrough innovations. You communicate clearly and effectively to share your findings with non-technical audiences.
* You are experienced in training language models or large computer vision models as well as have expertise in one or more key subdomains such as: training optimization, self-supervised learning, explainability, RLHF.
* You have an engineering mindset as shown by a track record of delivering models at scale both in training data and inference volumes. You have experience in delivering libraries, platforms, or solution level code to existing products.

Basic Qualifications:

* Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:

* A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 5 years of experience performing data analytics
* A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 3 years of experience performing data analytics
* A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field)

Preferred Qualifications:

* Master's Degree in "STEM" field (Science, Technology, Engineering, or Mathematics), or PhD in "STEM" field (Science, Technology, Engineering, or Mathematics)
* Experience working with AWS
* At least 3 years' experience in Python, Scala, or R
* At least 3 years' experience with machine learning
* At least 3 years' experience with SQL

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $158,600 - $181,000 for Princ Associate, Data Science

New York, NY: $173,000 - $197,400 for Princ Associate, Data Science

San Jose, CA: $173,000 - $197,400 for Princ Associate, Data Science

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website (https://www.capitalonecareers.com/benefits) . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.

No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com (mailto:RecruitingAccommodation@capitalone.com) . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com (mailto:Careers@capitalone.com)

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

<h5>Senior Data Scientist, Product Intelligence </h5><p>As a Senior Data Scientist for Product Intelligence, you will architect the analytical foundation for how fal builds and grows. Your domain spans the entirety of the developer journeyfrom initial acquisition to long-term expansion. You will turn raw behavioral and system signals into the metrics and insights that shape our product roadmap and go-to-market strategy. </p><p>This is a high-influence role where you will act as a strategic partner to Product, Engineering, and Marketing. Beyond domain-specific analysis, you will share ownership of the foundational data systems, standards, and experimentation frameworks that serve as the source of truth for the entire company. </p><h5>What You'll Do </h5><ul> <li>Architect the metrics that define user success on fal and build the measurement systems to track activation, retention, and time-to-value across both our self-serve and B2B pipelines. </li><li>Develop a deep, causal understanding of how customers discover, adopt, and grow on the platform to identify key expansion levers and acquisition opportunities. </li><li>Own the instrumentation standards and data contracts that serve as the source of truth for product telemetry and core behavioral schemas. </li><li>Surface insights through rigorous deep-dives and experimentation to influence product direction, pricing models, and go-to-market initiatives. </li><li>Develop scalable data products including automated experimentation frameworks and canonical schemas that enable teams to self-serve and move faster. </li></ul> <h5>What We Are Looking For </h5><ul> <li>5+ years of experience in data science or analytics rolesideally with API products, developer tools, or B2B SaaS platforms </li><li>Advanced-level SQL and proficiency in Python for analytics, modeling, and experimentation </li><li>Strong proficiency with dbt and orchestration tools like Dagster or Airflow for production pipeline </li><li>Proven ability to work across teams to influence product roadmap and strategy </li><li>Experience establishing data quality and governance practices, including lineage, testing, and documentation. </li><li>Demonstrated bias for action and the ability to thrive in fast-moving, ambiguous environments. </li><li>A track record of shipping data products or features, not just dashboards. You think like a Product Manager and build like an Engineer </li></ul> <h5>Nice-to-Haves </h5><ul> <li>Experience working on developer-facing products or platforms. </li><li>Exposure to infrastructure, performance, or cost-related data (e.g., cloud usage, compute efficiency). </li><li>Experience operating in early-stage or fast-scaling environments. </li><li>Interest or experience in architecting data systems designed for agentic consumption and AI-driven product workflow </li></ul> <h5>Compensation </h5><ul> <li>$190,000 - $230,000 + equity + comprehensive benefits package </li></ul> <h5>What We Offer At Fal </h5><ul> <li>Interesting and challenging work </li><li>A lot of learning and growth opportunities </li><li>We offer visa sponsorship and will help you relocate to San Francisco. </li><li>Health, dental, and vision insurance (US) </li><li>Regular team events and offsites </li> </ul>

Manager, Data Scientist - Credit Review

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description
In Capital One's Credit Review Models, Data and Innovative solutions team, we defend the company against model failures and find new ways of making better decisions with models. We use our statistics, software engineering, and business expertise to drive the best outcomes in both Risk Management and the Enterprise. We understand that we can't prepare for tomorrow by focusing on today, so we invest in the future: investing in new skills, building better tools, and maintaining a network of trusted partners.

We partner with best-in-class data scientists, analysts, credit risk management experts, and engineers to innovate solutions that directly impact the company's bottom line in a meaningful way. We do it all in a collaborative environment that values individual insight, encourages each associate to take on new responsibilities, promotes continuous learning, and rewards innovation.

Role Description
In this role, you will:

* Leverage a broad stack of technologies, such as, Python, Conda, AWS, H2O, Spark, and more, to reveal the insights hidden within huge volumes of numeric and textual data
* Build statistical and machine learning models to challenge the models in production
* Flex your interpersonal skills to translate the complexity of your work into tangible business goals
* Partner with a cross-functional team of data scientists, credit risk experts, and product managers to deliver a product customers love

The Ideal Candidate is:

* Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
* Statistically-minded. You've built models, validated them, and backtested them. You know how to interpret a confusion matrix or a ROC curve. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
* Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
* Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.

Basic Qualifications:

* Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:

* A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 6 years of experience performing data analytics
* A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 4 years of experience performing data analytics
* A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 1 year of experience performing data analytics

* At least 1 year of experience leveraging open source programming languages for large scale data analysis
* At least 1 year of experience working with machine learning
* At least 1 year of experience utilizing relational databases

Preferred Qualifications:

* PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics
* At least 4 years' experience in Python, Scala, or R for large scale data analysis
* At least 4 years' experience with machine learning
* At least 4 years' experience with predictive modeling

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Plano, TX: $179,400 - $204,700 for Mgr, Data Science

McLean, VA: $197,300 - $225,100 for Mgr, Data Science

Richmond, VA: $179,400 - $204,700 for Mgr, Data Science

Riverwoods, IL: $179,400 - $204,700 for Mgr, Data Science

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website (https://www.capitalonecareers.com/benefits) . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.

No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com (mailto:RecruitingAccommodation@capitalone.com) . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com (mailto:Careers@capitalone.com)

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

<h5>Senior Data Scientist </h5><p>Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their fans and build a lasting business including: paid memberships, free memberships, community chats, live video, and selling to fans directly with one-time purchases. Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with: </p><ul> <li>$10 billion+ generated by creators since Patreon's inception </li><li>100 million+ free memberships for fans who may not be ready to pay just yet, and </li><li>25 million+ paid memberships on Patreon today. </li></ul> <p>We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Senior Data Scientist to support our mission. </p><p>This role is based in New York or San Francisco and open to those who are able to be in-office 2 days per week on a hybrid work model. </p><h5>About the Role </h5><ul> <li>You'll work in lock step with your cross-functional team to build product features that help creators improve their membership monetization. </li><li>You'll conduct quantitative research to inform product strategy and opportunities, design metrics to help track goals and make decisions, devise learning methods (including experiments) to test hypotheses, and work closely with our machine learning and data engineering teams on building MVP user-facing data products. </li><li>You'll translate data into actionable insights and communicate your technical work to the data team, your cross-functional partners, executive leadership, and the rest of the company. </li><li>You'll be a cultural and technical leader on the data science team. </li></ul> <h5>About You </h5><ul> <li>Bachelor's degree in Statistics, Economics, Engineering, Mathematics, a related quantitative field, or equivalent practical experience. </li><li>You have a wealth of experience helping product teams make great decisions with data. </li><li>You are an experimentation expert and possess a very strong statistical inference skillset. </li><li>You have solid analytical thinking. You ask the right questions and strive to see beyond the numbers to understand what an analysis represents in terms of real-world applications. </li><li>You have experience making tradeoffs between speed and accuracy. </li><li>You are a SQL expert and have experience in Python or R to perform statistical analyses. </li><li>You have a track record of building strong relationships with product, engineering, research, and design teammates. </li><li>You have a growth mindset and aspire to continuously improve your soft and technical skills. </li><li>You are an expert communicator with a love for clarity and simplicity. </li></ul> <h5>About Patreon </h5><p>Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts: </p><ul> <li>Put Creators First They're the reason we're here. When creators win, we win. </li><li>Build with Craft We sign our name to every deliverable, just like the creators we serve. </li><li>Make it Happen We don't quit. We learn and deliver. </li><li>Win Together We grow as individuals. We win as a team. </li></ul> <p>We hire talented and passionate people from different backgrounds because workplace diversity and inclusion is critical to our ability to serve creators worldwide. If you're excited about a role but your past experience doesn't match with every bullet point outlined above, we strongly encourage you to apply anyway. If you're a creator at heart, are energized by our mission, and share our company values, we'd love to hear from you. </p><p>Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. If you need a reasonable accommodation during the interview process, please let us know via email at accomodations@patreon. </p><p>Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, flexible time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching. </p><p>Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office two days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates hired into remote-eligible roles are not expected to meet the same requirements. </p><p>At Patreon, we believe in fair and transparent pay. In compliance with New York and California pay transparency laws, we are sharing the expected salary range for this role. The posted salary range is dependent on the location and the level. This range may encompass multiple levels within the role's job family. The final offer will be based on candidate's experience, skills, competencies, and geographic location, aligning with the appropriate job level within Patreon's leveling framework. For remote employees located outside CA and NY, salary may vary based on location and local market conditions. </p><p>Patreon reserves the right to modify or update compensation and benefits at any time. </p>

<h5>Data Scientist/Analyst </h5><p>For the past 20 years, we have powered many Digital Experiences for the Fortune 500. Since 1999, we have grown from a few people to more than 4000 team members across the globe that are engaged in various Digital Modernization. </p><p>We are seeking a highly motivated and experienced Data Scientist/Analyst with a strong background in the Retail CPG industry to join our dynamic data team. In this role, you will be instrumental in transforming raw data into actionable insights, driving strategic decisions, and leveraging cutting-edge Agentic frameworks to automate and enhance data-driven processes. You will play a critical role in optimizing our supply chain, marketing strategies, and customer experience. </p><p>Responsibilities: </p><p>Data Cleansing and Preparation: </p><p>Develop and implement robust data cleansing and preprocessing pipelines to ensure data quality and consistency. </p><p>Extract, transform, and load (ETL) data from various sources, including POS systems, inventory management systems, and customer databases. </p><p>Identify and resolve data anomalies, inconsistencies, and missing values. </p><p>Create and maintain comprehensive data documentation. </p><p>Retail CPG Analysis: </p><p>Conduct in-depth analysis of sales, inventory, and customer data to identify trends, patterns, and opportunities. </p><p>Develop and implement predictive models for demand forecasting, inventory optimization, and customer segmentation. </p><p>Analyze the impact of marketing campaigns and promotional activities on sales and customer behavior. </p><p>Generate actionable insights and recommendations to optimize business performance. </p><p>Agentic Frameworks: </p><p>Research, evaluate, and implement Agentic frameworks to automate data analysis and decision-making processes. </p><p>Develop and deploy intelligent agents for tasks such as data extraction, anomaly detection, and report generation. </p><p>Integrate Agentic frameworks with existing data pipelines and analytical tools. </p><p>Explore and experiment with emerging AI technologies to enhance data-driven capabilities. </p><p>Collaboration and Communication: </p><p>Collaborate with cross-functional teams, including sales, marketing, and supply chain, to understand business requirements and deliver data-driven solutions. </p><p>Communicate complex data insights to stakeholders in a clear and concise manner. </p><p>Present data findings and recommendations through visualizations and reports. </p><p>Requirements: </p><p>Bachelor's or master's degree in data science, Statistics, Computer Science, or a related field. </p><p>Strong proficiency in SQL and Python (Pandas, NumPy, Scikit-learn). </p><p>Experience with data visualization tools (e.g., Tableau, Power BI). </p><p>Solid understanding of statistical modeling and machine learning techniques. </p><p>Proven ability to clean, process, and analyze large datasets. </p><p>Experience with ETL processes and data warehousing. </p><p>Strong analytical and problem-solving skills. </p><p>Excellent communication and presentation skills. </p><p>Preferred Qualifications: </p><p>Experience with Agentic frameworks (e.g., Auto-GPT, LangChain, etc.). </p><p>Familiarity with cloud platforms (AWS, Azure, GCP) and their data services. </p><p>Experience with time series analysis and forecasting. </p><p>Knowledge of supply chain management and inventory optimization. </p><p>Experience with customer segmentation and lifetime value analysis. </p><p>Knowledge of natural language processing (NLP) and text analysis. </p><p>Experience with LLMs. </p><p>Experience with Retail POS data. </p><p>Compensation, Benefits and Duration: </p><p>Minimum Compensation: USD 48,000Maximum Compensation: USD 168,000Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role. Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees. This position is available for independent contractors. No applications will be considered if received more than 120 days after the date of this post. </p>

Senior Manager, Data Scientist - US Card (Generative AI Systems)

Data is at the center of everything we do. As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988! Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.

As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.

Team Description
The Generative AI Systems (Genesis) team within Card Data Science builds state-of-art, generative AI-based solutions for dialogue, text summarization, reading comprehension, speech recognition, image/document processing as well as time-series sequencing modeling. We partner with product, tech and design teams to deliver internal applications based on these solutions that drive efficiency in our business and data analytics teams, as well as customer-facing applications that enhance the customer experience. You will work with a seasoned group of natural language processing (NLP), speech, and computer vision specialists, experimenting with emerging technologies in generative AI, delivering software implementing these technologies, and contributing research to major NLP and AI/ML conferences.

Role Description
In this role, you will:

* Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
* Leverage a broad stack of technologies — Python, Kubernetes, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
* Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
* Flex your interpersonal skills to translate the complexity of your work into tangible business goals

The Ideal Candidate is:

* Customer first. You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers.
* Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea.
* A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond.
* Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.

Basic Qualifications:

* Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date:

* A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 7 years of experience performing data analytics
* A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 5 years of experience performing data analytics
* A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics

* At least 2 years of experience leveraging open source programming languages for large scale data analysis
* At least 2 years of experience working with machine learning
* At least 2 years of experience utilizing relational databases

Preferred Qualifications:

* PhD in "STEM" field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
* At least 1 year of experience working with AWS
* At least 1 year of experience managing people
* At least 5 years' experience in Python, Scala, or R for large scale data analysis
* At least 5 years' experience with machine learning

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
McLean, VA: $225,400 - $257,200 for Sr Mgr, Data Science

New York, NY: $245,900 - $280,600 for Sr Mgr, Data Science

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website (https://www.capitalonecareers.com/benefits) . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.

No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com (mailto:RecruitingAccommodation@capitalone.com) . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com (mailto:Careers@capitalone.com)

Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.

Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

<h3>Job Description</h3><div>Job Description<div><strong>100% ONSITE</strong><br><br><b>Who We Are</b>: Founded in 2007, SimIS Inc. is an innovative information technology solution Veteran Owned Small Business (VOSB) that models future environments, requirements, and capabilities, and then secures the enterprise from internal and external threats compliant with Federal, State, and industry standard governance to ensure client mission success. Our performance standard is “excellence,” with an outcomes-based, quality focus in our services and products, guided by our core values of honesty (in word and deed), relationships (confidence and trust with clients and partners), teamwork (shared goals, mission, and purpose), loyalty (allegiance to our client and team), and importance of others (work and win as a team). SimIS is currently recruiting for the listed position. <br><br><b>Job Description:</b><br />NATO HQ Supreme Allied Command Transformation (NATO SACT) is seeking contract support to access to leading-edge capabilities necessary to adapt to future threats, integrate advanced technologies to modernize the force, explore long-term military strategy to shape how NATO's forces will operate in the future, and promote interoperability between current and next-generation platforms. To support our NATO customer, SimIS seeks a <strong>Senior Data Scientist</strong> to apply business and data manipulation principles and methods to technical problems to arrive at automated solutions and support the development and integration of emerging technologies to support NATO force design and strategic decision making.  <br><br><b>Education and Qualifications </b><ul><li>Master’s degree in Data Science, Computer Science, or Statistics.</li><li>8 years of data science experience in defense/security sectors.</li><li>5 years’ experience in designing, developing, and maintaining big data platforms/fabrics. </li><li>Experience in AI/ML and predictive analysis applications.</li><li>Expertise in Programming Languages: Python, R, SQL, Spark, <span class="long-word">TensorFlow/PyTorch.</span></li><li>Expertise in Data Visualization tools (Tableau, Power BI, D3.js).</li><li>Coding <span class="long-word">knowledge/experience:</span> Java, JavaScript, C, C++, etc.</li><li>A Data Science Professional Certificate is highly desirable. </li></ul><div><b>Responsibilities </b><ul><li>Support cloud-based big data design and development requirements for software prototypes, systems, and applications. </li><li>Provide cloud-based solutioning for internal R&D efforts to develop software prototypes and technical documentation.</li><li>Evaluate R&D prototypes and develop software technical documentation, specifications, and user guides.  </li><li>Develop and evaluate data requirements, perform data analysis on large data sets, translate raw data into usable knowledge and information, and provide expertise on creating usable data constructs.</li><li>Interpret results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently; designs, develops and implements the most valuable business cloud solutions for the organization.</li></ul><strong>Benefits:</strong><ul><li>Medical, Dental, and Vision</li><li>Short-Term Disability (at no cost to you) & Long-Term Disability</li><li>Life Insurance</li><li>401(k) Savings Plan</li><li>Flex Spending Accounts</li><li>Tuition Assistance Program</li><li>Professional Development</li><li>Paid Time Off (PTO)</li><li>11 Federal Holidays each year</li></ul><div>SimIS, Inc. is an EOE / M / F / Disability / VET / Drug Free Employer</div></div><br><br> <p>Powered by JazzHR</p><p>Dq4IgOmECV</p></div></div>

<h3>Job Description</h3><div>Job Description<div><div><b>Who We Are</b></div><div>ButcherBox is a certified B Corp headquartered in Boston, and honored to be celebrating our fourth year in a row on Inc.'s list of Best Workplaces. At ButcherBox, we believe in better. That’s why we deliver 100% grass-fed, grass-finished beef, free-range organic chicken, pork raised crate-free, and wild-caught seafood directly to our members’ doors. All of our products are humanely raised or wild-caught and never given antibiotics or added hormones ever. </div><div><br /></div><div>We’re working to build a world that’s better for all, and we’re inviting everyone to come along. For us, better means treating our planet with respect. It means improving the lives of animals and the livelihoods of farmers. It means never cutting corners when it comes to doing business. Ultimately, it means better meals, enjoyed together. Our team is made up of people who collaborate and support one another. We’re always looking for outstanding people to join our mission! </div><div><br /></div><div><b>About The Role</b> </div><div>We are seeking a Senior Data Scientist to uncover critical member insights from our marketing, operational, and financial data, driving smarter decisions and delivering exceptional offers, products, and services. The primary focus will be statistical analysis, high-quality predictive modeling, and end-to-end development of AI and ML systems integrated with our eCommerce platform, customer portal, and internal applications. </div><div><br /></div><div>Our data science projects range from demand forecasting to contextual multi-armed bandits for personalized recommendations, with data scientists supporting the full ML stack including contributing to the feature store and managing inference pipelines in production. We also partner closely with the marketing and commercial operations teams in designing experiments to measure model impact.  </div><div><br /></div><div>The ideal candidate will work from our Watertown, MA two day a week.</div><div><br /></div><div><b>Responsibilities</b> </div><div>•Partner cross-functionally with Marketing, Operations, Product, and Engineering teams to translate business needs into data-driven solutions </div><div>•Develop machine learning models and advanced analyses using Python and Spark SQL</div><div>•Own projects from ideation and stakeholder engagement through production deployment and impact measurement </div><div>•Provide analytical thought leadership, propose new data experiments, and identify opportunities for measurable impact </div><div>•Champion data quality, reproducibility, and scalable code using best practices in version control (GitHub) and cloud infrastructure (Azure) </div><div>•Mentor junior Data Scientists, Data Engineers, and Analytics Engineers to develop their technical and business acumen </div><div>•Present complex findings and actionable recommendations to diverse audiences, including senior leadership </div><div><br /></div><div><b>Requirements</b> </div><div>We know that many people come to data science from non-linear career paths and educational backgrounds, and that job titles don’t always capture what your work involved, but to be successful in this role you need to have:  </div><div><br /></div><div>•3+ years of proven experience developing and supporting machine learning models to solve complex business problems</div><div>•7+ years of proven experience solving complex business problems with analytics, statistical analysis, and coding (Python, R, SQL) in a business environment</div><div>•Advanced knowledge of statistical modeling: <span class="long-word">linear/multivariate</span> models, hypothesis testing, time series analysis </div><div>•Familiarity with personalization and reinforcement learning such as multi-armed bandits, propensity modeling, Markov decision processes and discrete choice modeling, AND/OR familiarity with causal inference methods such as uplift modeling, instrumental variables, diff-in-diff, fixed effects regression, synthetic controls and double ML  </div><div>•Track record of delivering robust, production-grade data solutions in a cloud environment </div><div>•Strong communication skills—able to translate results and recommendations for non-technical stakeholders </div><div>•Hands-on experience with large datasets, cloud platforms (Azure), and version control (GitHub) </div><div>•Demonstrated commitment to code quality and reproducible research </div><div><br /></div><div><b>Preferred Qualifications</b> </div><div>•Evidence of using analytics to influence business strategy and drive measurable outcomes </div><div>•Experience solving marketing, subscriber growth, or operational problems in e-commerce or similar domains </div><div>•Experience in causal inference</div><div>•Experience in personalization modeling and reinforcement learning </div><div>•Experience with MLOps</div><div>•Success mentoring and developing data science talent </div><div>•Interest in sustainability, food systems, or mission-driven work aligning with ButcherBox values </div><div>•Ability to commute to Watertown, MA at least once a week</div><div><b>What We Offer</b></div><div>Our Values </div><div>We are a mission-driven company and strive to embody our values in every step of the process. ButcherBox is driven by relentless improvement, accountability, humility, and customer obsession. We want our workplace to be one of growth and learning where everyone can be safe/comfortable to show up as their authentic self to work. Anyone who loves our products and goals is welcome here! We are proud to be an equal opportunity employer aspiring to incite positive change in the pastures, the fulfillment centers, the office, and the world. </div><div><br /></div><div>Benefits</div><div>•Health: medical, dental, vision, and life insurance, an employer-funded HSA, and short & long-term disability benefits</div><div>•Financial: 401(k) with generous employer match</div><div>•Annual bonus based on company performance</div><div>•Time off: unlimited paid time off policy, 20 weeks parental leave, an executive team that encourages a good work/life balance</div><div>•Personal growth: Annual Learning & Development stipend, and access to a range of personal and professional coaching solutions through BetterUp</div><div>•Food: a free Custom Classic ButcherBox each month and weekly lunch reimbursement</div><div>•Others: reimbursement for wellness activities, an exceptional EAP, fertility benefits, life insurance and more</div><div><br /></div><div><i>ButcherBox is an equal opportunity employer committed to fostering a diverse, inclusive environment at all steps in the hiring process. Every qualified applicant will receive consideration without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. We are happy provide reasonable accommodations during our hiring process. Please click here if you need to request an accommodation for your interview.</i></div><p>ButcherBox does not use artificial intelligence (AI) tools in any part of our hiring process. If you would like more information about how your data is processed, please contact us.</p></div></div>

<h5>Senior Data Scientist </h5><p>PLUM is a fintech company empowering financial institutions to grow their business through a cutting-edge suite of AI-driven software, purpose-built for lenders and their partners across the financial ecosystem. We are a boutique firm, where each person's contributions and ideas are critical to the growth of the company. </p><p>This is a fully remote position, open to candidates anywhere in the U.S. with a reliable internet connection. While we gather in person a few times a year, this role is designed to remain remote long-term. You will have autonomy and flexibility in a flat corporate structure that gives you the opportunity for your direct input to be realized and put into action. You'll collaborate with a high-performing team including sales, marketers, and financial services experts who stay connected through Slack, video calls, and regular team and company-wide meetings. We're a team that knows how to work hard, have fun, and make a meaningful impactboth together and individually. </p><h5>Job Summary </h5><p>We are looking for a Senior Data Scientist to lead the development of scalable Generative AI pipelines that process raw data and generate context-aware results to power Plum's AI-driven products. You will play a central role in shaping our GenAI platform, working across the full ML lifecyclefrom ingestion and retrieval to generation, evaluation, and deployment. </p><p>This role combines deep expertise in machine learning with hands-on experience in building production-grade systems. You'll collaborate closely with various cross functional teams and operate in a fast-paced environment where innovation, autonomy, and ownership are key. </p><h5>Key Responsibilities </h5><ul> <li>Design and architect end-to-end Generative AI pipelines using LLMs to process and generate context-aware results. </li><li>Integrate open-source and proprietary LLMs (e.g., GPT, LLaMA) via APIs and custom orchestration. </li><li>Build and optimize workflows using frameworks such as LangChain </li><li>Design and implement RAG (Retrieval-Augmented Generation) architecture to inject relevant, contextual data into generation prompts. </li><li>Develop robust methods to evaluate and compare LLM outputs based on relevance, personalization, and factual accuracy. </li><li>Build automated and scalable LLM evaluation pipelines using embedding-based similarity, scoring metrics, and human-in-the-loop feedback. </li><li>Implement monitoring, observability, and logging for GenAI workflows to ensure reliability in production. </li><li>Collaborate with cross-functional teams to integrate generative outputs into client-facing applications. </li> </ul>

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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