Reddit Data Scientist Interview Questions + Guide in 2024

Formerly nicknamed “the front page of the internet,” Reddit is among the most visited websites in the world with over 2 billion hits in December last year. The platform thrives on the power of data, and its team of data scientists plays a crucial role in harnessing that power.

From user behavior analysis to recommendation engine optimization, Reddit’s data scientists are at the forefront of the platform’s innovative progression. As a data scientist at Reddit, you’ll leverage your expertise to uncover hidden patterns, personalize experiences, and drive the platform’s continued growth.

Cracking the code for a data science role at Reddit can be thrilling but challenging. This guide will serve as your comprehensive roadmap, delving into the specific Reddit data scientist interview questions you might encounter and equipping you with the strategies you need to showcase your skills and secure your dream job.

The interview process for a data scientist role at Reddit is designed to assess your technical skills, analytical thinking, cultural fit, and alignment with the company’s values and mission. Here’s an expanded overview of the entire process, from initial screening process to onboarding:

Initial Screening Process

Once your application is submitted, Reddit’s recruitment team reviews it to ensure your qualifications meet the job requirements. Key factors include educational background, relevant work experience, and technical skills.

If you are shortlisted in the resume review, you’ll be contacted by a recruiter for an initial phone screening. This call usually lasts around 30 minutes and covers your background information, motivations for applying, and an overview of the interview process. During this time, feel free to ask questions you have about the role.

Technical Interview Rounds

Depending on the position, you may be required to complete an online assessment testing your coding skills and knowledge of data science concepts. This might include problems related to statistics, probability, data manipulation, and machine learning algorithms. Product sense interview questions might also be asked.

If you perform well on the assessment, you’ll be invited to a technical phone interview with a data scientist from the team. This interview covers coding, data analysis, and problem-solving. You may be asked to work through problems in Python, SQL, or another relevant language.

On-site Interview Loop

Successful candidates from the technical phone interview are invited to on-site interviews, which can also be conducted virtually. These typically consist of multiple rounds of interviews, each lasting about an hour. The interviews focus on various aspects, including problems related to data structures, algorithms, and general programming skills, as well as questions that assess your ability to analyze data, build models, and interpret results.

A deeper discussion on how to design data systems and pipelines, scalability issues, and integration with existing infrastructure will be conducted during this Reddit data scientist interview round. The interviewers will also evaluate your cultural fit, communication skills, and collaborative approach within a team.

Partner Interview Rounds

In some cases, you might have a final interview with a senior executive or the head of the data science department. This interview focuses on your long-term goals, alignment with Reddit’s mission, and how you envision contributing to the company.

If you perform well across all rounds, you’ll be extended a job offer. The offer includes details about the role, compensation, benefits, and other relevant information. You’ll typically be given some time to consider the offer and negotiate if necessary.

Here are a few Reddit data scientist interview questions that you’ll find relevant and useful:

1. What would your current manager say about you? What constructive criticisms might he give?

2. How comfortable are you presenting your insights?

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

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

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

6. Let’s say you designed an experiment to measure financial rewards’ impact on users’ response rates. The result shows that the treatment group with $10 rewards has a 30% response rate, while the control group without rewards has a 50% response rate. Can you explain what happened and how you could improve this experimental design?

7. Let’s say that you work at Reddit. The CEO wants to increase the overall engagement on Reddit. They decided that one way to do this was to build a “hot posts” or “trending posts” sort feature. How would you approach the problem and build this feature if you were in charge of leading the project?

8. Let’s say you work at a social media website. You want to build a system that will automatically detect the topic of new posts, but several thousand posts are created every hour, not to mention all the posts already on the website. How would you go about creating a machine learning model to classify posts by topic given the scale of the data?

9. How would you design an A/B test to determine if a new feature improves user engagement on Reddit? What metrics would you use, and how would you ensure statistical significance?

10. Describe a situation where you performed hypothesis testing. What was the hypothesis, how did you test it, and what were the results?

11. Explain how you would use linear regression to predict user activity on Reddit. What features would you consider, and how would you handle potential multicollinearity?

12. How would you choose between using a decision tree, random forest, and gradient boosting machine for a classification problem? What factors would influence your decision?

13. Describe when you had to create new features for a machine learning model. How did you identify these features, and how did they impact model performance?

14. How do you evaluate the performance of a machine learning model? What metrics do you consider and why?

15. Write an SQL query to find the top 10 subreddits with the highest average number of comments per post over the past month.

16. Describe the process you would follow to clean a dataset that contains missing values, duplicate entries, and outliers.

17. How would you design a data pipeline to process and analyze user interaction data from Reddit? Include data ingestion, storage, processing, and analysis in your answer.

18. Describe how you would build a recommendation system for Reddit to suggest relevant subreddits to users based on their activity.

19. We’re given three tables representing a forum of users and their comments on posts. Write a query to get the top three users with the most upvotes on their comments written in 2020.

20. Find the three best-performing days recorded for each advertiser who achieved the highest weekly revenue in 2021.

21. We are given three tables about a digital community, users, comments, comment_votes, representing a forum of users and their comments on posts. We want to determine if bad actor users create multiple accounts to upvote their comments. What kind of metrics could we use to figure this out? Write a query that could display the percentage of users on our forum that would be acting fraudulently in this manner.

Example:

Input:

users table

comments table

comment_votes table

Output:

22. You’re given three tables. An ads table holds an ID and the advertisement name like “Labor Day shirts sale”. The feed_comments table holds the comments on ads by different users that occur in the regular feed. The moments_comments table holds the comments on ads by different users in the moments section.

Write a query to get the percentage of comments by ad that occur in the feed versus mention sections of the app.

Example:

Input:

feed_comments table

moments_comments table

ads table

Output:

Reddit seeks both technical and behavioral proficiency from its data scientist candidates. Here is how to prepare for your upcoming Reddit data scientist interview:

Understand Reddit’s Culture and Mission

Before diving into technical preparation, familiarize yourself with Reddit’s mission, values, and culture. Understanding the company’s goals and how your role as a data scientist would align with them is crucial. Browse through Reddit’s official website, read up on recent news, and check out the “About Us” section to understand their core values.

Spend time on Reddit to understand its structure, popular subreddits, and the type of content shared. Being an active user can provide insights into the user experience and areas where data science can be applied.

Brush Up on Data Science Fundamentals

Refresh your knowledge of the foundational skills for any data scientist. These include key statistical concepts such as hypothesis testing, confidence intervals, regression analysis, and probability distributions. In addition, revisit common machine learning algorithms like linear regression, logistic regression, decision trees, random forests, gradient boosting, k-means clustering, and neural networks. Make sure that you understand their use cases, advantages, and limitations.

Additionally, ensure you are proficient in data manipulation techniques using tools like pandas for Python. Practice cleaning, transforming, and analyzing datasets. Data querying is a vital skill. Practice writing complex SQL queries to extract, filter, join, and aggregate data from relational databases.

Practice Coding Skills

Most data science interviews at Reddit will require proficiency in Python or R. Practice solving problems on our platform. Focus on writing clean, efficient code. Also, work on problems that involve data structures (arrays, lists, dictionaries) and algorithms (sorting, searching, dynamic programming). Furthermore, learn how to optimize your code and queries.

Refine System Design Concepts

Learn about designing data pipelines, including data ingestion, ETL (extract, transform, load) processes, and ensuring data quality. To crack data scientist interviews at Reddit, understand how to design systems that can scale efficiently to handle large volumes of data. This includes knowledge of distributed computing frameworks like Hadoop and Spark.

Prepare for Behavioral Questions

As mentioned, Reddit intensely prioritizes behavioral competency. Use the STAR (situation, task, action, result) method to structure your answers to data science behavioral questions. Practice describing past experiences where you showcased problem-solving skills, teamwork, leadership, and how you handled challenging situations.

Reflect on why you want to work at Reddit and how your values align with the company’s mission. Be prepared to discuss how you can contribute to their goals.

Practice Case Studies

Analyze case studies related to Reddit’s business model, which is available on our platform. Think about how you would use data science to solve user engagement, content recommendation, and spam detection issues. Also, work on personal or open-source projects to build a strong portfolio. This demonstrates your practical experience and problem-solving abilities.

Mock Interviews

Conduct mock interviews with friends or colleagues who have experience in data science interviews. This can help you learn to articulate your thought process and receive feedback. Consider simulating interview conditions using our P2P mock interview feature.

What is the average salary for a data scientist role at Reddit?

The average base salary for a reasonably experienced data scientist at Reddit is around $189,000, and the average total compensation is about $106,000. See our data scientist salary guide to get a better idea of the industry standards.

What other companies besides Reddit are hiring data scientists?

Data scientists are highly valued and well-compensated at modern companies that recognize the importance of clean data and insightful analysis. You might consider exploring data scientist roles at social media companies like Meta, Twitch, and Quora.

Does Interview Query have job postings for the Reddit data scientist role?

Yes, we have job postings for Reddit data scientist roles on our job board. Follow this interview guide and apply for the job that tickles your fancy.

By honing the technical skills and interview strategies detailed in this guide, you’ll be well-prepared to impress Reddit’s interview team and secure your ideal data scientist position. If you’re interested in exploring other roles, such as data engineer, data analyst, or software engineer, check out our comprehensive Reddit Interview Guide.

Reddit values candidates who are passionate about data analysis, adept at problem-solving, and skilled at translating insights into actionable strategies. Refine your expertise, demonstrate your enthusiasm, and get ready to contribute to Reddit’s mission of fostering vibrant online communities. Good luck!