The Trade Desk Data Scientist Interview Questions + Guide in 2024

The Trade Desk is a global technology company with a mission to create a better, more open internet for everyone through principled, intelligent advertising. Handling over 1 trillion queries per day, their platform operates at an unprecedented scale and has fostered an award-winning culture of trust, ownership, empathy, and collaboration.

As a Data Scientist at The Trade Desk, you will be responsible for developing, researching, and deploying advanced models to solve complex advertising challenges. Expect to navigate sparse and noisy data, large output spaces, and real-time processing needs in a dynamic and globally connected team. The Trade Desk values inclusive workspaces and is consistently ranked among the best workplaces globally.

This guide will walk you through their interview process, commonly asked The Trade Desk data scientist interview questions, and tips to prepare. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a The Trade Desk data scientist interview:

Data-Driven Take-Home Assignment

After your application is shortlisted, you’ll receive a data-driven take-home assignment. This assignment is designed to assess your practical skills using real data that is closely related to the company’s work. You typically have one week to complete this assignment, although completing it earlier can make a good impression.

Onsite Interview Rounds

Once you successfully submit the take-home assignment, you’ll be invited to attend onsite interview rounds. These rounds will consist of multiple interviews over the course of a day, and the interviewing team will likely include individuals from different departments.

The key focus areas will include:

• Behavioral Questions: To understand your cultural fit and prior experiences.
• Technical Questions: Covering concepts such as deep learning, machine learning algorithms, data manipulation, and statistical methods.
• Real-Time Problem Solving: Questions about handling and analyzing large datasets, building models, and developing solutions under constraints such as high QPS and millisecond latency requirements. Collaboration:** How you interact with engineering teams, participate in ideation, productionalization, and product monitoring.

Typically, interviews at The Trade Desk vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

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

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

2. Write a function to merge two sorted lists into one sorted list.

Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

3. Create a function missing_number to find the missing number in an array.

You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

4. Develop a function precision_recall to calculate precision and recall metrics from a 2-D matrix.

Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

5. Write a function to search for a target value in a rotated sorted array.

Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, return its index; otherwise, return -1. Bonus: Your algorithm’s runtime complexity should be in the order of (O(\log n)).

6. Would you think there was anything fishy about the results of an A/B test with 20 variants?

Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with the results?

7. How would you set up an A/B test to optimize button color and position for higher click-through rates?

A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

8. What would you do if friend requests on Facebook are down 10%?

A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

9. Why would the number of job applicants decrease while job postings remain the same?

You observe that the number of job postings per day has remained constant, but the number of applicants has been decreasing. What could be causing this trend?

10. What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis?

You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in “messy” datasets.

11. How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment?

You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice for this problem?

12. How does random forest generate the forest, and why use it over logistic regression?

Explain how a random forest algorithm generates its forest. Additionally, why might you choose random forest over logistic regression for certain problems?

13. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms. In which scenarios would you prefer a bagging algorithm over a boosting algorithm? Provide examples of the tradeoffs between the two.

14. How would you justify using a neural network model and explain its predictions to non-technical stakeholders?

If your manager asks you to build a neural network model to solve a business problem, how would you justify the complexity and explain the model’s predictions to non-technical stakeholders?

15. What metrics would you use to track the accuracy and validity of a spam classifier for emails?

Assume you have built a V1 of a spam classifier for emails. What metrics would you use to evaluate its accuracy and validity?

16. Is this a fair coin given 8 tails and 2 heads in 10 flips?

You flip a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair based on this outcome.

17. How do you write a function to calculate sample variance for a list of integers?

Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places.

18. How do you find the median of a list with more than 50% of the same integer in O(1) time?

Given a sorted list of integers where more than 50% of the list is the same integer, write a function to return the median value in O(1) computational time and space.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your The Trade Desk data scientist interview include:

• Thoroughly Understand the Company Mission: The Trade Desk aims to create a better, more open internet through principled, intelligent advertising. Understanding the company’s mission and culture can help you provide relevant and aligned responses.
• Brush Up on Technical Skills: Ensure you are comfortable with deep learning technologies such as TensorFlow or PyTorch, distributed computing platforms like EMR or Databricks, and programming languages like Python, R, or SQL.
• Prepare for Practical Assignments: Your take-home assignment and onsite problem-solving tasks will likely involve real-world data and scenarios. Practice solving these types of problems with a focus on accuracy and efficiency.

What is the average salary for a Data Scientist at The Trade Desk, Inc.?

What kind of work does a Data Scientist do at The Trade Desk?

Data Scientists at The Trade Desk are responsible for developing algorithms for their real-time bidding platform to help advertisers run effective campaigns. Their work involves deep learning, handling sparse or noisy data, and optimizing models under millisecond latency requirements. Data Scientists participate actively from the ideation phase to production and monitoring, ensuring end-to-end ownership of data-focused projects.

What qualities is The Trade Desk looking for in a Data Scientist?

The Trade Desk values candidates with a sustained track record of making significant contributions to machine learning projects. They seek individuals with a strong sense of data intuition, ability to innovate, and product-focused mindset. Candidates should be collaborative, confident, and thrive in a diverse and inclusive environment. Intellectual curiosity, ability to learn quickly, and effective communication skills are also crucial.

What skills and technologies should I be familiar with to be a Data Scientist at The Trade Desk?

Candidates should have experience with deep learning technologies such as TensorFlow or PyTorch, and running large-scale workloads on distributed computing clusters using technologies like Spark and Databricks. Proficiency in programming languages such as Python, R, Java, or SQL is important, along with a good understanding of software engineering concepts like containerization and version control.

The journey to becoming a Data Scientist at The Trade Desk is a rewarding experience that challenges you both technically and personally. From an engaging take-home assignment rooted in real company data to the welcoming nature of the interviewers, you’ll find yourself in an environment that genuinely values your unique contributions.

For deeper insights and thorough preparation, check out our main Trade Desk Interview Guide, featuring numerous questions that might come your way. We’ve also crafted guides for other positions, including software engineer and data analyst, where you can get a more comprehensive look at the interview processes for these roles.

Good luck with your interview!