Prepare for and practice interview questions from Guideline.

Guideline Interview Questions

guideline 401(k). helping people save for a better future. guideline is the only 401(k) provider that doesn’t charge participants any fees on investments, regardless of the value of their assets or retirement account balance. and for employers, guideline charges a low, flat rate fee per participant, in contrast to the asset-based fee model predominant in the industry. by offering a full-stack solution that automates plan administration and compliance, the guideline team is combatting what’s unfortunately become the retirement industry status quo -- legacy providers have put profit over the interests of employees, abandoning the goal of the program in the first place: to give employees the opportunity to save as much as possible for retirement.

guideline 401(k). helping people save for a better future. guideline is the only 401(k) provider that doesn’t charge participants any fees on investments, regardless of the value of their assets or retirement account balance. and for employers, guideline charges a low, flat rate fee per participant, in contrast to the asset-based fee model predominant in the industry. by offering a full-stack solution that automates plan administration and compliance, the guideline team is combatting what’s unfortunately become the retirement industry status quo -- legacy providers have put profit over the interests of employees, abandoning the goal of the program in the first place: to give employees the opportunity to save as much as possible for retirement.

Overview

Guideline Interview Guides

Data Structures & Algorithms

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

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

String Shift

Minimum Change

Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N.

Sum to N

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Bank Fraud Model

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

Why would one algorithm generate different success rates with the same dataset?

Same Algorithm Different Success

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

Rolling Bank Transactions

Probability

Given three uniform(0,4) random variables, what is the probability that the median of them is greater than 3?

Median Probability

Flipping 576 Times

HHT or HTT

Mutated Offspring

Coin Flip Probability

A/B Testing

Experiment Validity

Button AB Test

Green Dot

Sample Size Bias

Testing Price Increase

Challenge

When an interviewer asks a question along the lines of:

<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>

How would you respond?

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

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

Your Strengths and Weaknesses I

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

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

When an interviewer asks you a question along the lines of:

<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>

How should you respond?

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?

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

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

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

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

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

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

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

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

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

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

P-value to a Layman I

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

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

What are the assumptions of linear regression?

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

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

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

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

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

Give an example of the tradeoffs between the two.

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

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

Bagging vs. Boosting

Bagging vs Boosting

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<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>
<code>departments</code> table

<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>
Output:

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

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

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.

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

Bias vs. Variance Tradeoff

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

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

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

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

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

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

Average Trees

Random Forest Explanation

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

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

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

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

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

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

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.

How would you set up this test?

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.

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

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?

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?

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.

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.

How would you assess the validity of the result?

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 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.

How would we build this model?

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?

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?

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.).

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

<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>

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

Note: Please return only one query with each number in a different row

Example:

Input:

<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>
Output:

<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

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

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

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

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

Prime to N

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

When should you use one versus the other?

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

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

Regularization Example

Regularization and Validation

What’s the difference between Lasso and Ridge Regression?

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

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

Lasso vs Ridge

<h1>Binary Tree Validation</h1>

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

A valid BST is defined as follows:

<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>

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.

<h3>Example:</h3>

Input:

<img src="https://d2qpirhrfplx04.cloudfront.net/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

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

Binary Tree Validation

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

Making data-driven insights actionable for those without technical expertise

Simple Explanations

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

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

Docs Metrics

Product Sense & Metrics

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

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.

How would you go about diagnosing and improving the performance of this query?

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

Slow SQL Query

Query Optimization

Let’s say you work as a data scientist at a bank.

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan they are taking out.

<ol>
<li>How would you evaluate whether using a decision tree algorithm is the correct model for the problem?</li>

<li>Let’s say you move forward with the decision tree model. How would you evaluate the performance of the model before deployment and after?</li>
</ol>

Decision Tree Evaluation

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you. About the RoleWe’re looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. You’ll play a central role in shaping Guideline’s data products — helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases. This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market. The ideal candidate combines strong analytical skills with a product-first mindset—someone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release. Key Responsibilities<ul><li>Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production. </li><li>Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness </li><li>Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams. </li><li>Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases. </li><li>Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence. </li><li>Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams. </li><li>Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals. </li><li>Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation.</li><li>Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline.</li><li>Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights.</li><li>Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance</li></ul>Compensation: We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits: Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to:<ul><li>Medical, Dental, Vision, Health Savings Account, Flexible Spending Account</li><li>STD, Life, LTD and AD&D</li><li>401(k) with a company match program</li><li>Unlimited Paid Time Off (PTO)</li><li>Paid Parental Leave</li><li>Commuter Benefits</li><li>Employee Recognition Program</li><li>Referral Bonus Program</li></ul>Equal Opportunity Employer: Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:Required Skills & Experience<ul><li>Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact. </li><li>Strong analytical and problem-solving skills — able to identify trends, anomalies, and opportunities in large datasets. </li><li>Proficiency in SQL for querying, transforming, and validating complex datasets. </li><li>Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik). </li><li>Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations. </li><li>Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines. </li></ul>Preferred Skills & Experience<ul><li>Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets. </li><li>Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms. </li><li>Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality. </li><li>Knowledge of JIRA, Confluence, and agile product development practices. </li><li>Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders. </li><li>Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms).</li><li>Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations.</li><li>Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required).</li><li>Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required).</li></ul></div></div>

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you.About the RoleWe are looking for a Senior Data Analyst who is highly practical, detail-oriented, and strong in Python- and SQL-based data manipulation. This role is hands-on and focused on cleaning, standardizing, and enriching large, messy advertising spend datasets—especially digital data sourced from MediaOcean’s Prisma platform, where key information often lives inside inconsistent free-form text fields.In addition to data transformation and quality work, you’ll apply statistical and machine learning techniques to forecast spend trends, fill data gaps, and classify unstructured text data. You’ll also be expected to stay aware of emerging AI-assisted and generative AI technologies, understanding how they can improve analysis and automation even if you’re not directly building such models. What You’ll DoData Cleaning, Parsing & Standardization (Core Function)<ul><li>Build Python (Pandas/Regex) workflows to extract structure from free-form Prisma text fields containing placements, publishers, formats, devices, tactics, etc.</li><li>Develop rule-based and pattern-based logic to recognize abbreviation variations, naming inconsistencies, and agency-specific formatting habits.</li><li>Create normalization scripts that standardize publisher names, platform types, channels, and other key taxonomies.</li><li>Maintain “classification dictionaries” and pattern libraries that improve accuracy over time.</li></ul>Data Quality & Operational Support<ul><li>Profile incoming agency data to identify common quality issues and propose concrete remediation.</li><li>Design QA checks to detect missing fields, malformed entries, inconsistent dimensional labels, and mismatched spend totals.</li><li>Partner closely with Data Engineering to embed your logic into scheduled pipelines.</li><li>Own recurring operational workflows that support data ingestion, aggregation, and quality monitoring.</li></ul>Dimensional Enrichment, Modeling & Data Derivation<ul><li>Build and maintain predictive models to support media spend forecasting, pricing analysis, and text classification for data tagging and enrichment.</li><li>Apply statistical modeling or regression techniques to impute missing values or infer incomplete spend or performance metrics.</li><li>Derive new fields—such as format groupings, platform classifications, creative types, and targeting attributes—using pattern recognition and model-based inference across text fields.</li><li>Produce structured dimensions from unstructured data to improve downstream reporting.</li><li>Document the logic and assumptions behind model-driven derivations to ensure transparency and reproducibility.</li></ul>Analysis & Internal Stakeholder Support<ul><li>Investigate anomalies, unexpected trends, and data gaps by tracing issues back to source fields.</li><li>Build dashboards that help internal teams observe data quality, volume, deviations, and model-driven insights.</li><li>Provide ad-hoc deep dives into digital spend patterns, forecast accuracy, and classification performance for product, ops, and leadership.</li></ul>Why This Role MattersYour work directly determines the quality, usability, and credibility of the datasets our company provides to clients and uses internally. This is a core operational and analytical role, focused on making messy agency data usable at scale—while adding new analytical capabilities through modeling, forecasting, and intelligent automation. Compensation We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to:<ul><li>Medical, Dental, Vision, Health Savings Account, Flexible Spending Account</li><li>STD, Life, LTD and AD&D</li><li>401(k) with a company match program</li><li>Unlimited Paid Time Off (PTO)</li><li>Paid Parental Leave</li><li>Commuter Benefits</li><li>Employee Recognition Program</li><li>Referral Bonus Program</li></ul>Equal Opportunity Employer Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:What You Bring<ul><li>5+ years of experience as a Data Analyst or Data Operations Analyst.</li><li>Expert-level Python (Pandas, Regex) and SQL.</li><li>Experience extracting meaning from unstructured or text-heavy data.</li><li>Working knowledge of forecasting, regression, and basic statistical modeling (e.g., ARIMA, Prophet, linear regression, or similar methods).</li><li>Familiarity with text classification / NLP workflows (e.g., scikit-learn, spaCy, Hugging Face).</li><li>Awareness of AI-assisted development tools and basic understanding of generative AI / LLM concepts and their applications in data enrichment and automation.</li><li>Strong understanding of data hygiene, classification logic, and dimensional modeling.</li><li>Comfortable working in data warehouses (Snowflake, Redshift, etc.).</li><li>Ability to create repeatable, documented transformation logic.</li></ul></div></div>

<h3>Job Description</h3><div>Job Description<div>Description:About GuidelineAt Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience.If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you. About the RoleWe’re looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. You’ll play a central role in shaping Guideline’s data products — helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases.This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market.The ideal candidate combines strong analytical skills with a product-first mindset—someone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release. Key Responsibilities<ul><li>Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production.</li><li>Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality — all to drive successful adoption and launch readiness</li><li>Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams.</li><li>Design and implement automated data validation and quality frameworks for digital products — defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases.</li><li>Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence.</li><li>Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams.</li><li>Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals.</li><li>Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation.</li><li>Partner with engineering teams to integrate machine-learning–based anomaly detection and data quality checks into the product pipeline.</li><li>Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights.</li><li>Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance</li></ul>Compensation: We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. Benefits: Guideline offers full- time employees a comprehensive benefits package based on location. Some benefits may include, but are not limited to: - Health, dental, life, and disability insurance - RRSP with company match - Paid time off and parental leave - Teledoc Health services - Employee recognition and referral bonuses Equal Opportunity Employer: Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply.Requirements:Required Skills & Experience<ul><li>Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact.</li><li>Strong analytical and problem-solving skills — able to identify trends, anomalies, and opportunities in large datasets.</li><li>Proficiency in SQL for querying, transforming, and validating complex datasets.</li><li>Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik).</li><li>Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations.</li><li>Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines.</li></ul>Preferred Skills & Experience<ul><li>Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets.</li><li>Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms.</li><li>Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality.</li><li>Knowledge of JIRA, Confluence, and agile product development practices.</li><li>Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders.</li><li>Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms).</li><li>Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations.</li><li>Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required).</li><li>Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required).</li></ul></div></div>

Job Type Full-time <h5>Description </h5><h5>About Guideline </h5>At Guideline.ai, we're defining the future of advertising. By harnessing data transparency and advanced tools, we empower marketers to make smarter, faster, and more profitable decisions. We work with the world's top brands, agencies, and media owners to transform media buying and selling into a more intelligent, efficient experience. If you're ready to be part of a high-growth company at the cutting edge of data, AI, and media, we'd love to meet you. <h5>About the Role </h5>Were looking for a Senior Data Product Analyst who thrives at the intersection of data, analytics, and product development. Youll play a central role in shaping Guidelines data products helping to transform large, complex datasets into actionable insights, power new features, and ensure the reliability of our data across production and releases. This role combines analytical depth with product collaboration: working with engineers, product managers, and data strategists to evaluate datasets, automate QA workflows, and bring new capabilities to market. The ideal candidate combines strong analytical skills with a product-first mindsetsomeone who can turn data into strategy, shape product direction, and ensure the integrity of the data powering every release. <h5>Key Responsibilities </h5><ul> <li>Partner with Product and Engineering teams to research, evaluate, and develop new data-driven products and feature enhancements, conducting deep analyses on complex spend, pricing, and performance datasets to identify patterns, assess data viability, and inform design decisions from concept through production. </li><li>Contribute to go-to-market release execution by validating data in production environments, coordinating QA and feature testing, preparing product collateral, and delivering internal demos to align stakeholders on new functionality all to drive successful adoption and launch readiness </li><li>Support release management by validating production data, investigating anomalies, providing analytical context for product and customer teams, and coordinating release communications to facilitate transparency and smooth deployment across teams. </li><li>Design and implement automated data validation and quality frameworks for digital products defining thresholds, conditional formatting, and anomaly detection logic to proactively flag and resolve data issues, streamline QA processes, and ensure accuracy and reliability across releases. </li><li>Lead critical business-as-usual operations for monthly data releases, ensuring quality, reliability, and on-time delivery for customers by driving validation efforts, coordinating stakeholders, and resolving issues efficiently to support client retention and confidence. </li><li>Champion best practices in data governance, validation, and experimentation, establishing standards and documentation that enhance data quality, reliability, and analytical rigor across teams. </li><li>Play an active role in PI Planning, shaping backlog priorities, validating scope and acceptance criteria, and ensuring coordination across Product and Engineering teams to set realistic and value-driven delivery goals. </li><li>Leverage AI-powered analytical tools and LLM-based workflows to accelerate data validation, insight generation, and documentation. </li><li>Partner with engineering teams to integrate machine-learningbased anomaly detection and data quality checks into the product pipeline. </li><li>Identify product opportunities where AI or predictive modeling can enhance user experience, improve accuracy, or surface actionable insights. </li><li>Evaluate dataset readiness for AI/ML use cases, ensuring structures, definitions, and data governance support high-quality model performance </li></ul> <h5>Compensation </h5>We consider a wide range of factors when determining compensation including relevant experience, education, and skill level. <h5>Benefits </h5><ul> <li>STD, Life, LTD and AD&D </li><li>401(k) with a company match program </li><li>Unlimited Paid Time Off (PTO) </li><li>Employee Recognition Program </li><li>Referral Bonus Program </li></ul> <h5>Equal Opportunity Employer </h5>Guideline is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities, and veterans to apply. <h5>Requirements </h5><h5>Required Skills & Experience </h5><ul> <li>Extensive experience as a Data Analyst in a data-centric or product-driven environment, with the ability to translate analytical insights into product strategy and measurable business impact. </li><li>Strong analytical and problem-solving skills able to identify trends, anomalies, and opportunities in large datasets. </li><li>Proficiency in SQL for querying, transforming, and validating complex datasets. </li><li>Hands-on experience with Power BI or equivalent BI tools (Tableau, Looker, Sisense, Qlik). </li><li>Demonstrated ability to communicate complex findings clearly, collaborate effectively with cross-functional teams, and influence decisions across Product, Engineering, and Operations. </li><li>Highly organized, detail-oriented, and proactive in managing multiple priorities and deadlines. </li></ul> <h5>Preferred Skills & Experience </h5><ul> <li>Background in media, advertising, or marketing data, with an understanding of spend, pricing, or campaign datasets. </li><li>Familiarity with MediaOcean (Prisma, Spectra), HudsonMX, or WideOrbit platforms. </li><li>Experience implementing automated QA systems, anomaly detection, or alerting frameworks for data quality. </li><li>Knowledge of JIRA, Confluence, and agile product development practices. </li><li>Strong data storytelling and business acumen, able to synthesize insights and present recommendations to senior stakeholders. </li><li>Familiarity with AI-assisted analytics tools (e.g., ChatGPT, Copilot, AI-based BI platforms). </li><li>Understanding of how data pipelines support AI models, including data labeling, data quality, and governance considerations. </li><li>Exposure to LLM workflows, embeddings, or vector-based search concepts (nice to have, not required). </li><li>Experience with Python-based data analysis or lightweight ML experimentation (scikit-learn preferred but not required). </li></ul> #J-18808-Ljbffr

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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