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Drivetime Interview Questions

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Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Making data-driven insights actionable for those without technical expertise

Simple Explanations

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

Why Do You Want to Work With Us

How would you negotiate and resolve disagreements when a client rejects your proposed solution?

Client Solution Pushback

Analytics

What strategies could we try to implement to increase the outreach connection rate through analyzing this dataset?

Outreach Strategy

Choosing Between Python and SQL

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

A/B Testing

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

How would you approach limited testing time before a major app release?

Testing Constraints

Business Case

How would you set and calculate the optimal monthly rent price, including from tenant bids

Apartment Pricing

How would you optimize and try to choose the highest value artwork

Most Valuable Artwork

Data Pipelines

Design a data pipeline to compute hourly, daily, and weekly active user metrics from a data lake for an hourly-refreshing dashboard

Data Pipelines and Aggregation

How would you build an ETL pipeline to ingest Stripe payment data into a warehouse for revenue dashboards and analytics

Payment Data Pipeline

Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?

Say you work for an e-commerce website that receives clickstream data from Kafka on a daily basis. You’re asked to design a data analytics solution for storing and querying raw data, with a two-year retention period.

The current data volume is around 600 million events daily.

What kind of data analytics solution would you design, keeping costs in mind?

Design a cost-conscious analytics solution to store and query daily Kafka clickstream data with two-year retention

Clickstream Data

Data Modeling

You’re a data scientist at YouTube. Leadership wants to revisit how the platform measures success, not for a specific new feature, but for the product as a whole given its current state.

How would you define and measure success for YouTube? Identify your primary and secondary metrics, explain your rationale for each, and discuss how you would choose the right thresholds for any engagement-based metrics you propose.

The role of A/B testing in measuring the success rate of an analytics experiment

Success Measurement

Product Sense & Metrics

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?

Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

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

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

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

Suppose we have a binary classification model that classifies whether or not an applicant should be qualified to get a loan.

Because we are a financial company, we have to provide each rejected applicant with a reason why.

Given we don’t have access to the feature weights, how would we give each rejected applicant a reason why they got rejected?

In the context of a loan eligibility model, what does a partial dependence plot represent?

In the context of a loan eligibility model, what does a partial dependence plot represent?

Rejection Reason

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

Machine Learning

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.

<ol>
<li>What’s the expected value of how long someone will have to wait before they receive the test?</li>

<li>What is the likelihood they get selected after the first day? Is that closer to 0 or 1?</li>
</ol>

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.What's the expected value of how long someone will have to wait before they receive the test?

Suppose there are one million users and we want to expose 1000 users per day to a test. The same user can be selected twice for the test.What's the expected value of how long someone will have to wait before they receive the test?

Expected Tests

Probability

Let’s say we’ve proposed a new analytics dashboard to a client, but after reviewing our design and data sources, the client says the solution doesn’t meet their needs and pushes back on several key features.

How would you approach the conversation to negotiate alternative solutions, and what steps would you take to ensure disagreements are handled constructively so we can still reach a successful outcome?

Let’s say you work for a bank that gives out personal loans. Your co-worker develops a model that takes in customer inputs and returns if a loan should be given or not.

<ol>
<li>What kind of model did the co-worker develop? 
</li>
<li>Another co-worker thinks they have developed a better model to predict defaults on the loans. Given that personal loans are monthly installments of payments, how would you measure the difference between the two credit risk models within a timeframe? 
</li>
<li>What metrics would you track to measure the success of the new model?</li>
</ol>

What kind of model did the co-worker develop, how would you measure the difference between the two credit risk models within a timeframe, and what metrics would you track to measure the success of the new model

Loan Model

Let’s say we work at L’Oréal, and the e-commerce team has noticed that about 8% of online beauty product orders are delivered late each week.

How would you frame this as a data science problem, choose an algorithm to address it, and describe your plan for deploying the solution?

How would you solve for late deliveries using data science at L'Oréal?

Late Deliveries

Supply Chain

Let’s say we’re launching a new version of the PayPal app, but due to a tight deadline, we don’t have time to run the entire suite of 300 automated test cases before release.

How would you handle this situation?

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?

Let’s say you’re an apartment building manager setting unit prices for a new apartment building complex.

<ol>
<li>What considerations would you make in setting monthly rent prices for the apartment? 
</li>
<li>How might you calculate the optimal rent price from those considerations?</li>
<li>If you implement a tenant bidding system, how do you calculate the optimal price from those bids? </li>
</ol>

You’re given 100 pieces of artwork one by one in random order. Each piece of artwork is worth a different dollar amount.

Your goal is to pick the piece of art with the highest dollar amount. However, you can’t go back once you pass a piece of art and you can only choose one.

How would you optimize and try to choose the highest value artwork?

Let’s say that Facebook account managers are not able to reach business owners after repeated calls to try to onboard them onto a new Facebook business product.

Let’s say that we have training data on all of the account manager’s outreach in terms of calls made, calls picked up, time of call, etc…

What strategies could we try to implement to increase the outreach connection rate through analyzing this dataset?

You need to preprocess a dataset containing millions of mortgage records stored in a relational database. Would you use Python or SQL for this, and why?

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

Drivetime Interview Questions

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Drivetime Interview Questions

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Discussion & Interview Experiences