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Machine Learning

How do we give each rejected applicant a reason why they got rejected?

Rejection Reason

Job Recommendation

Lasso vs Ridge

Bagging vs Boosting

Type-ahead Search

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

Empty Neighborhoods

Customer Orders

Last Transaction

Manager Team Sizes

Create a report displaying which shipments were delivered to customers during their membership period.

Completed Shipments

A/B Testing

Button AB Test

Network Experiment Design

Testing Price Increase

Free Shipping Mention Test

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

Statistically Significant Test

Product Sense & Metrics

Group Success

Instagram TV Success

Uber Eats Success

Docs Metrics

Measure Facebook Stories success by tracking reach, engagement, and actions aligned with specific business goals

Facebook Story Success

Data Structures & Algorithms

Write a function to return a dataframe containing every transaction with a total value of over $100.

Over 100 Dollars

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

Prime to N

Reconstruct the path of a trip so that the trip tickets are in order.

Scrambled Tickets

Valid Anagram

Closest Key

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

Behavioral

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

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

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

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

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

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

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

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Analytics

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?

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?

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>

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

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

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?

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?

How would you tackle multicollinearity in multiple linear regression?

Multicollinearity in Regression

In the context of hypothesis testing, what are type I errors (type one errors) and type II errors (type two errors)? What is the difference between the two?

Bonus: Describe the probability of making each type of error mathematically.

What is the difference between type I and type II errors?

Type I and II Errors

Let’s say you want to test the close friends feature on Instagram Stories.

How would you make a control group and test group to account for network effects?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

Network Experiment Design I

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

Network Experiment Design II

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

Network Experiment Design III

Given two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.

Note: A word is not an anagram of itself.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;listen&#34;
string_2 = &#34;silent&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>True
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;banana&#34;
string_2 = &#34;bandana&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>False
</code></pre>

Let’s you’re tasked with pitching a new feature for Google Home. Your co-worker comes to you with an idea to build a game feature for Google Home.

How would you go about deciding whether Google should build it?

Game Feature Home

Business Case

Let’s say that you’re the data scientist for the marketing/advertising division of your company. The marketing executive wants to test multiple new channels including:

<ul>
<li>Youtube ads</li>
<li>Google search ads</li>
<li>Facebook ads</li>
<li>Direct mail campaigns</li>
</ul>

Given these new marketing channels, how would you design an a/b test to utilize the marketing budget in the most efficient way possible?

Marketing Dollar Efficiency

Implement the k-means clustering algorithm in python from scratch, given the following:

<ul>
<li>A two-dimensional NumPy array <code>data_points</code> that is an arbitrary number of data points (rows) <code>n</code> and an arbitrary number of columns <code>m</code>.</li>
<li>Number of k clusters <code>k</code>.</li>
<li>The initial centroids value of the data points at each cluster <code>initial_centroids</code>.</li>
</ul>

Return a list of the cluster of each point in the original list data_points with the same order (as a integer).

Example

<img src="https://i.ibb.co/KKp2gPn/kemans-before.png" alt="before clustering"/>

After clustering the points with two clusters, the points will be clustered as follows.

<img src="https://i.ibb.co/bdwVWxR/kemans-example-after.png" alt="after clustering"/>

Note: There could be an infinite number of separating lines in this example.

Example

<pre tabindex="0" class="chroma"><code>
#Input
data_points = [(0,0),(3,4),(4,4),(1,0),(0,1),(4,3)]
k = 2
initial_centroids = [(1,1),(4,5)]


#Output 

k_means_clustering(data_points,k,initial_centroids) -&gt; [0,1,1,0,0,1]

</code></pre>

<img src="https://i.ibb.co/pRsRz12/example.png" alt=""/>

Implement the k-means clustering algorithm in python from scratch

k-Means from Scratch

An ecommerce company is experiencing a reduction in revenue for the past 12 months. You have the following transaction data:

<ul>
<li>Date of sale</li>

<li>Total $ Amount paid by customer</li>

<li>Profit Margin per unit</li>

<li>Quantity of item</li>

<li>Item category</li>

<li>Item subcategory</li>

<li>Marketing attribution source</li>

<li>% discount applied</li>
</ul>

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

Evaluating Revenue Decline

Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.

How would you build this data pipeline?

Design a data pipeline for hourly user analytics.

Data Pipelines and Aggregation

Data Pipelines

How would you measure the success of Facebook Groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Write a Python function called <code>longest_increasing_subsequence</code> that takes a list of integers as input and returns as output the length of the longest increasing subsequence in the list.

Note: An increasing subsequence is a subset of a list where the elements are in strictly increasing order. The subsequence does not have to be contiguous or unique.

<h3>Example:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [10, 9, 2, 5, 3, 3, 7, 101, 18]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>4
</code></pre>

Explanation:

The longest increasing subsequence is <code>[2, 3, 7, 101]</code>, for a total length of 4.

Find the longest increasing subsequence in a list of integers.

Longest Increasing Subsequence

Challenge

<h5>Senior Data Engineer </h5>Here at Discount Tire, we celebrate the spirit of our people with extraordinary pride and enthusiasm. Our business has been growing for more than 60 years and now is the best time in our history to join us. We recognize that to remain the industry leader we must continue to grow and evolve our business in a rapidly changing industry. The Business Analytics team, one of the fastest growing teams in the company, is leading this change. We are responsible for driving the insights, recommendations, and developing the decision support tools that influence the strategic direction of the company. Under minimal supervision, the Senior Data Engineer designs, builds, and operates scalable data solutions on Discount Tire's Databricks-based lakehouse platform. This role supports enterprise analytics, business intelligence, and data science initiatives by delivering high-quality, governed, and performance data products across Omni-channel domains. The Senior Data Engineer is responsible for developing and maintaining ELT pipelines, data models, and lakehouse architecture using modern cloud and big data technologies, with a strong focus on reliability, performance, and production ownership. <h5>Essential Duties and Responsibilities </h5>Lakehouse & Data Architecture <ul> <li>Designs, develops, and maintains Databricks-based lakehouse solutions leveraging medallion architecture (Bronze, Silver, Gold) and Delta Lake. </li><li>Builds scalable, high-performance analytical data models to support reporting, analytics, and downstream consumption. </li><li>Ensures data solutions align with enterprise standards for security, governance, scalability, and cost efficiency. </li></ul> Data Engineering & ELT Pipelines <ul> <li>Develops, tests, and maintains batch and streaming ELT pipelines using Spark SQL. </li><li>Implements incremental data processing, dependency management, and idempotent pipeline patterns across multiple data sources. </li><li>Integrates structured and semi-structured data from transactional, digital, and third-party systems into the lakehouse. </li></ul> Databricks Platform Development <ul> <li>Develops and maintains Databricks notebooks, workflows, and jobs across development, QA, and production environments. </li><li>Leverages Delta Lake features including ACID transactions, schema enforcement and evolution, and time travel. </li><li>Partners with platform and DevOps teams to support environment promotion, scheduling, and deployment automation. </li></ul> Data Quality & Reliability <ul> <li>Implements automated data quality checks, validation frameworks, and monitoring to ensure accuracy, completeness, and timeliness of data. </li><li>Proactively identifies and resolves data issues, ensuring reliable data availability for business partners. </li></ul> Performance Optimization & Tuning <ul> <li>Troubleshoots data pipeline and query performance issues and implements optimizations related to Spark execution, partitioning, file sizing, and data layout. </li><li>Documents root cause analysis and corrective actions and provide recommendations to improve system performance and reliability. </li></ul> Production Ownership & Operations <ul> <li>Owns data pipelines end-to-end in production, including monitoring, alerting, incident response, and operational support. </li><li>Contributes to runbooks, operational documentation, and continuous improvement of production readiness practices. </li></ul> Programming & Standards <ul> <li>Writes clean, maintainable, and well-tested code using Python (PySpark) and SQL. </li><li>Contributes to data engineering standards, reusable frameworks, and best practices across the Analytics Engineering team. </li></ul> Documentation & Reviews <ul> <li>Produces clear technical documentation, including design specifications and data flow diagrams. </li><li>Participates in design reviews and code reviews, providing constructive feedback to ensure quality and consistency. </li></ul> Collaboration & Stakeholder Engagement <ul> <li>Collaborates closely with data engineers, analytics engineers, data scientists, digital analysts, and business stakeholders to translate business needs into scalable data solutions. </li><li>Works cross-functionally with project managers, platform teams, and external partners to deliver high-impact analytics capabilities. </li></ul> Mentorship & Technical Leadership <ul> <li>Mentors and supports less experienced data engineers through technical guidance and best-practice sharing. </li><li>Leads technical design discussions for complex datasets and influences architectural decisions within the team. </li></ul> Continuous Improvement <ul> <li>Stays current with emerging data engineering technologies, trends, and industry best practices. </li><li>Identifies opportunities to improve efficiency, reliability, and scalability across data engineering workflows. </li></ul> Performs other duties as assigned. <h5>Qualifications </h5>5+ years of experience in data engineering, data integration, or analytics engineering roles. Experience with cloud-based analytics platforms and modern data architecture. Technical Skills <ul> <li>Advanced experience with SQL and Spark (PySpark and Spark SQL) in large-scale data environments. </li><li>Strong experience designing and maintaining ELT pipelines in a cloud-based lakehouse architecture. </li><li>Deep understanding of data modeling for analytics, including star schemas and slowly changing dimensions. </li><li>Experience with streaming or near-real-time data processing is preferred. </li><li>Strong knowledge of data governance, security, and compliance best practices. </li></ul> Tools and Platforms <ul> <li>Extensive experience with Databricks and Delta Lake for analytics and data engineering workloads. </li><li>Experience with cloud object storage platforms such as AWS S3. </li><li>Familiarity with orchestration, scheduling, and monitoring of data pipelines. </li><li>Experience working with large, complex datasets in analytics and reporting contexts. </li><li>Familiarity with BI and visualization tools such as Tableau, Looker, or Power BI. </li><li>Experience with digital analytics data (e.g., Adobe Analytics or Google Analytics) is a plus. </li></ul> Methodologies and Frameworks <ul> <li>Experience working within SDLC methodologies such as Agile and Waterfall. </li><li>Familiarity with CI/CD and version control practices for data engineering workflows. </li></ul> Communication and Presentation Skills <ul> <li>Ability to clearly communicate complex technical concepts to technical and non-technical audiences, including leadership. </li><li>Strong written and verbal communication skills, including documentation and presentation of technical solutions. </li></ul> Customer Service & Collaboration <ul> <li>Demonstrated ability to work collaboratively across teams and with business stakeholders. </li><li>Strong customer service mindset with a focus on delivering reliable and high-quality data solutions. </li></ul> Adaptability & Problem Solving <ul> <li>Strong troubleshooting and analytical skills with a proactive, solution-oriented approach. </li><li>Ability to work effectively in a fast-paced, evolving environment and manage multiple priorities. </li></ul> Curiosity <ul> <li>Intellectual curiosity and passion for data, analytics, and continuous learning. </li></ul> <h5>Educational Requirements </h5>Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent practical experience. Data or cloud-related certifications are a plus. <h5>Work Days </h5>Normal work days are Monday through Friday. Occasional Saturdays and Sundays may be necessary. <h5>Work Hours </h5>Normal work hours are 8:00 a.m. to 5:00 p.m. Additional hours may be necessary. Discount Tire provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type 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 law.

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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