Tinder, the ubiquitous dating app with 75 million active users, is on a mission to keep the magic of human connection alive. The company logs every click, meaning they have a huge volume of data to work with, and every product launch, from small to large, must be tested before launch. If you’re a data scientist keen on joining a genuinely data-driven company, Tinder could be a great career fit. Get ready for your data science interviews with Exponent's [Data Science Interview Course](https://www.tryexponent.com/courses/data-science). It features a wide selection of mock interview videos, interview rubrics, answer frameworks, and real-world practice questions from data science candidates and interviewers at Google, Amazon, and other top tech companies. ## What does a Tinder data scientist do? Because Tinder’s main product is its data-heavy mobile app, data science at Tinder takes on a role similar to a product analyst at Google or a [product growth analyst at Meta](https://www.tryexponent.com/guides/meta-product-growth-analyst-interview). The focus is on long-term growth, and the work is a potent combination of data science, [product sense](https://www.tryexponent.com/blog/improve-your-product-sense), experimentation, and analysis. Tinder’s data science team partners closely with product managers to understand challenges, translate business problems into data problems, and develop novel ways to gather information, test hypotheses, and make good decisions. Overall, the data science team is focused on mobile app product development. As one current data scientist says, “Data scientists at Tinder are partners with cross-functional departments, and use data to help make informed product decisions and strategies.” To that end, day-to-day work as part of Tinder’s data science team involves: - __Experiment design and analysis:__ Designing and evaluating A/B tests for new projects, deriving user insights to inform product launch decisions, etc. - __Exploratory data analysis:__ Forming hypotheses, exploring large datasets to understand user behaviors, identifying opportunities for product improvements, etc. - __Ad-hoc data requests:__ Partnering with product managers to solve product problems or tackling specific data tasks. For example, you might build a dashboard to track the performance of new projects or build a model to understand how different metrics are related. - __Stakeholder management:__ Proactively communicate with stakeholders to understand the underlying problems they are trying to solve, translate those business problems into data problems, communicate options, and align on objectives. These tasks are reflected in open job postings. For example, a junior data scientist job posting at Tinder lists the following responsibilities: - Digest large, complex transaction datasets into distilled analytics that help the business identify new opportunities. - Quickly investigate KPI movements in an evolving global dataset, disentangling correlation and causation to communicate resolutions efficiently. - Collaborate closely with strategy and finance teams to study worldwide online dating trends and provide detailed market research to guide pricing decisions. - Drive ownership of your own projects while balancing speed of execution and business value. - Be an authority on where and how to organize our data and readily share insights with data scientists, product managers, and finance leaders as well as at a company level. - Define and operationalize the detailed tracking of company-wide, team-specific, and product-specific performance metrics via rollout tables, dashboards, and automated reporting. This guide was written with the help of a data scientist at Tinder. ## What are the typical job requirements for a Tinder data scientist? At minimum, data scientists at Tinder must have: - A solid knowledge of probability and statistics - Strong SQL skills - Experience with experiment design and analysis - Some experience with product development Here are some of the basic requirements we’ve seen listed in various Tinder data science roles: - Expertise with SQL and data visualization tools (e.g., Tableau, Mode, Databricks) - Ability to join multiple tables and data sources into an aggregated form that drives business understanding - Ability to design and execute complex A/B tests and statistical experiments on large datasets - Experience with a scripting language (Python, R, and/or Spark) and respective data analysis libraries - Eagerness to improve our data infrastructure and how we report analytics to the broader organization - Comfort using descriptive statistics - Curiosity of how things work, from both a business and technical perspective - A collaborative growth mindset with a dedication to community-based projects and partnerships - Experience working on data related to finance and/or subscription business models is a plus. Browse [openings at Tinder](https://www.lifeattinder.com/#open-positions) for role-specific insights. Since Tinder is smaller than FAANG companies, it’s challenging to find salary benchmarks but compensation is significant. - For __junior data scientists__ with 2-4 years of experience, total compensation is __$182K__ with $140K base salary and a $17K bonus. - __Senior data scientists__ with >5 years at Tinder can make __$210K__ in total compensation. - Data science managers can earn anywhere from __$246K-$297K__ depending on their location. ## Recommendations before you apply for Tinder data science roles __Revamp your resume.__ Communication skills are critical at Tinder and [your resume](https://www.tryexponent.com/resume) is the perfect way to demonstrate this from the beginning. Make sure you can tell a coherent, compelling story around all the experiences listed on your resume and why you’re an ideal candidate for Tinder. Recent interviewees at Tinder suggest that you include the following in your resume: - __Degrees/certifications:__ A B.S. in a computational field is ideal. If you don’t have the degree, mention any statistics classes taken or showcase your skills through projects. - __Programming:__ SQL is important, and you should know the basics of R and/or Python. - __A/B testing:__ Highlight experience with basic experiment design (especially A/B testing) and analysis. - __Project/work experience:__ Product analysis or data science experience directly related to product is ideal. If you don’t have this, showcase your data analysis experience or projects. For example, machine learning (ML) projects, exploratory data analyses, and examples of KPI dashboards you’ve made will help you stand out. - __Advanced analytics:__ This section is optional, but demonstrating experience with ML modeling and causal inference can be a differentiator. __Practice with mock interviews.__ [Exponent's coaching services](https://www.tryexponent.com/coaching) are your best friend. Don’t limit your pool of mock partners to other data scientists and peers in tech—grab a non-tech friend and describe the most recent project you spearheaded. Communicating effectively with both your peers in engineering and non-technical collaborators will be critical for your growth on the job. __Lean on your community.__ Take what you learned from your team research and connect with a few data scientists or product managers at Tinder on Exponent or LinkedIn and ask them about their experiences. They’ve gone through what you’re going through now, so they’re great sources of information and support. ## Interview Process Landing a role in data science at Tinder involves going through: - A __phone screen__ with a recruiter. - An __(optional) take-home assessment__. For example, that might be a data exploratory analysis based on a sample of real Tinder data, along with SQL coding questions. - A __conversation with the hiring manager__ to explain the role, assess experience, and test essential skills. - A __virtual onsite__ consisting of four to five rounds, including technical, behavioral, product sense/collaboration, and analytical/case questions. Overall, the process is quick. Expect a final decision within a few weeks. ## Recruiter Phone Screen Tinder’s recruiter screen typically lasts for about 30 minutes with some time reserved at the end for your questions. The purpose of this call is to ensure that both the candidate and the hiring team are aligned on expectations and general fit. Your recruiter will explain the role, describe the interview process in more detail, and ask questions to gauge whether you’re a potential match. Common questions include: The recruiter screen is a great opportunity to gather information about the company, the team, the work, and the upcoming interview process, so be sure to prepare your own questions. ## Take-Home Assessment (Optional) Tinder’s data science team is small, and hiring managers have the option of assigning take-homes. Typically, the purpose is to filter out “low-intent” applicants. In other words, the actual assignment isn’t tricky, though it’ll take a few hours. You’ll have a week to complete the take-home. What do these take-homes typically entail? One hiring manager we spoke to offers a sanitized Tinder dataset and asks candidates to conduct an exploratory data analysis. Others assign take-homes that assess basic probability and statistics knowledge. In any case, if you take the time to turn in a reasonable answer, you’ve met the goal—which is to signify yourself as a high-intent candidate. If you receive a take-home assessment, we recommend asking yourself: - What does the data look like? How many variables are there, and are they continuous or discrete? How many observations are there? - How clean is this data? Are there missing or duplicate values? Where did the data come from? Do I need to transform any variables before diving into analysis? - What patterns exist? Are there any noticeable trends or relationships between variables? Is seasonality present? - Is there additional data I would need to do a more thorough analysis? What additional information would allow me to draw strong conclusions? Standard answers include a set of descriptive statistics and an analysis of what data points are moving over time. ## Hiring Manager Screen Next is a quick call with the hiring manager. This lasts 30 to 60 minutes and is similar to the recruiter screen but in greater depth. Common questions include: - Walk me through an A/B test you worked on. - How would you measure the performance of a new feature? - How would you explain p-value to a nontechnical collaborator? You’ll walk through your resume and then get questions meant to assess your technical background, as well as behavioral questions assessing your culture fit and potential to be a good collaborator. Expect to: - Describe your technical experience, emphasizing specific experience doing data analysis for product development and optimization. Bonus points for mobile product experience. - Answer questions about what motivates you and why Tinder is a great fit. - Describe what you’re looking for in a data science role and what you can personally bring to the team. - Answer questions about what you think of Tinder. What works well? What could be improved? ## Virtual Onsite Interview If you pass the recruiter screen, the take-home, and the hiring manager call, you’ll be invited to a virtual onsite interview with three to five rounds. Expect to face: - A __technical__ round with a senior analyst or data scientist to assess your technical skills, basic stats and probability knowledge, and experimentation design/analysis. - An __analytics/case__ round where you’re given a few short scenarios and asked how you would respond. Product sense is assessed as well. - A __behavioral__ round assessing culture fit and leadership skills. Senior candidates will face more interviews and go into more depth—for example, there may be a stakeholder interview where a typical stakeholder (such as a product leader) will assess your product sense and collaboration/leadership skills. Junior candidates will face product sense and behavioral questions in the above rounds, but they won’t be expected to go into as much detail. ### Technical Round The technical interview runs 45 to 60 minutes. A potential team member will ask questions to assess your: - Overall technical skillset - Knowledge of basic statistics and probability - Ability to draw actionable insights from data Remember that data science at Tinder is very product-centric. Hiring managers report that technical interviewers ask questions based on real problems data scientists face in their day-to-day. This is a conversational interview; you’ll discuss resume projects, answer hypothetical questions, and even answer light case questions. Prepare to explain how you gather information before making decisions, the group dynamics at play in previous data science roles, and how you collaborate effectively with stakeholders. There are no tricks here. Below are sample resume review questions you can expect: For more practice, Exponent has an interview prep course dedicated to [technical resume reviews](https://www.tryexponent.com/courses/engineering-management/project-retro/retro-intro). Besides talking through your resume, you should also prepare to answer technical questions on: - __Product metrics:__ What metrics would you use to determine success for a given project, and why? - __Experimental design and analysis:__ Let’s say we’re exploring a few options as we prepare to launch a new feature. How would you design an A/B test to determine which option we should go with? - __Exploratory data analysis:__ Given a hypothetical problem with Tinder’s mobile app and a sample Tinder dataset, brainstorm some hypotheses about what could be going wrong. How would you manipulate this dataset to answer your questions? What additional data would you need to validate your hypotheses? What areas for improvement do you see? - __KPI dashboards:__ Let’s say you were tasked with building a dashboard to track the performance of a new product launch. What KPIs would you include? How would you query an SQL database to give you the answers you’re looking for? - __Stakeholder management:__ Let’s say that a product manager approached you, concerned about decreasing usage. What questions would you ask to understand the business problem better, and how would you translate that into a data problem? The best way to prepare for technical questions like these is to review basics, and then get hands-on practice. Start with a review of the following basic concepts. __Basic probability and statistics__ - [Linear regression](https://www.tryexponent.com/courses/data-science-interview/data-science/linear-regression) - [The bias-variance tradeoff](https://www.tryexponent.com/courses/data-science-interview/data-science/bias-variance-netflix) - [Splitting datasets for testing](https://www.tryexponent.com/courses/data-science-interview/data-science/split-dataset-training-evaluation-testing) __Coding in SQL, R and/or Python__ - [SQL Foundations](https://www.tryexponent.com/courses/sql-foundations) - [Learning Python for Data Science](https://www.dataquest.io/blog/how-to-learn-python-for-data-science-in-5-steps/) - [Coursera’s Data Analysis with R Course](https://www.coursera.org/specializations/statistics?utm_medium=sem&utm_source=gg&utm_campaign=B2C_NAMER_statistics_duke_FTCOF_specializations_country-US-country-CA&campaignid=19997217497&adgroupid=147571863545&device=c&keyword=coursera%20data%20analysis%20with%20r%20specialization&matchtype=b&network=g&devicemodel=&adposition=&creativeid=670156515494&hide_mobile_promo&gclid=Cj0KCQiApOyqBhDlARIsAGfnyMo38AudGymCiE1l2m1n7TGKJE4YEEys3TtsMm83Ab5AKGi5gt7KKtgaArnAEALw_wcB) __Experimental design and analysis__ - [How to Design and Run Experiments](https://www.tryexponent.com/courses/design-run-experiments) - [A/B Testing an Email Campaign](https://www.tryexponent.com/courses/data-science-interview/data-science/ab-testing-email) - [Analyzing A/B Testing Significance](https://www.tryexponent.com/courses/data-science-interview/data-science/ab-testing-significance) __Data exploration and generating insights__ - [Coursera’s Exploratory Data Analysis Course](https://www.coursera.org/learn/exploratory-data-analysis?utm_medium=sem&utm_source=gg&utm_campaign=B2C_NAMER_jhu-data-science_jhu_FTCOF_specializations_country-US-country-CA&campaignid=313639147&adgroupid=121203872804&device=c&keyword=&matchtype=&network=g&devicemodel=&adposition=&creativeid=507187136063&hide_mobile_promo&gclid=Cj0KCQiApOyqBhDlARIsAGfnyMpb0xGMStpSq5K4HupsW6Gob1OoSAZ487U5KUIGli8jFFcXkM_cW8kaArSKEALw_wcB) - [freeCodeCamp’s Data Analysis with Python Course](https://www.youtube.com/watch?v=r-uOLxNrNk8) - [Exploratory Data Analysis in Python](https://www.youtube.com/watch?v=Liv6eeb1VfE) __Product Sense and Product Metrics__ - [Towardsdatascience’s Series on Product Sense](https://towardsdatascience.com/tagged/product-sense) - [(Podcast) The Data Scientist Show Episode #13: Developing Product Sense](https://podcasts.apple.com/id/podcast/develop-product-sense-to-uplevel-your-data-science/id1584430381?i=1000542245205) - [Mock Interview: Improving Snap Camera Speed](https://www.tryexponent.com/courses/data-science-interview/data-science/snap-camera-speed) If you’re looking for more practice, we recommend [booking a coaching session](https://www.tryexponent.com/coaching?category=mock_interviews&src=nav&role=data-science) with a data scientist. Your coach can assess your skills, make recommendations for your prep, and coach you to improve your answers. ### Analysis/Case Question Round After the technical interview comes at least one analysis/case question interview, which assesses your ability to think through data science questions from end to end. Junior candidates generally face one round, but the total number ultimately varies based on the team and your seniority level. For analysis/case questions, interviewers want to know that you can: - Translate business questions to data questions thoughtfully. - Demonstrate an ability to design solid experiments, showcasing competence with stats and probability theory. - Choose an appropriate set of metrics to monitor success. - Implement your suggestions in code. You’ll have 45 to 60 minutes to answer various questions based on a specific case. Generally, you’ll be asked questions that are directly relevant to Tinder, but this isn’t guaranteed. Sample questions include: - How would you measure the success of a new feature, such as the [profile quiz](https://techcrunch.com/2023/11/20/tinder-redesigns-profile-pages-with-prompts-info-tags-and-quiz/)? - A key Tinder messaging metric is down by 10%. What would you do to investigate? - What metrics would you use to understand customer’s overall satisfaction with Tinder? - [If you were the PM for Lyft, what dashboard would you build to track the health of the app?](https://www.tryexponent.com/questions/84/lyft-pm-dashboard-app-health) - [Define success for TikTok.](https://www.tryexponent.com/questions/1535/define-success-for-tik-tok) - [How can Stripe use data to predict the best time to retry a transaction?](https://www.tryexponent.com/questions/1674/how-can-stripe-use-data-to-predict-the-best-time-to-retry-a-transaction) - [We’re running an A/B test in 20 countries after adding a new feature. 1 of the 20 countries shows a significant preference for one version over the other. How would you interpret these results? What follow-up action would you take?](https://www.tryexponent.com/courses/data-science-interview/data-science/ab-testing-significance) To ace your analysis/case interview rounds, we recommend following this 5-step framework: - First, __define the problem__. Ask clarifying questions until you thoroughly understand the parameters of the problem you’re trying to solve. - Then __hypothesize__. Don’t grasp a possible explanation before laying out a few reasonable hypotheses given your prompt. Your hypotheses should be reasonable according to the context you’ve gathered. Present a few initial ideas—but don’t choose yet. - Next, __evaluate your options and prioritize a hypothesis to test__. Check your hypotheses against the parameters you’ve already identified. What makes the most sense? What is the simplest, most likely solution to test? Be sure to explain your thought process. You may revisit this step again if you’re wrong the first time. This is expected. - Then __design an experiment__ you could run to test your hypothesis. Be sure to describe the sample size you’d need, and the length of your test. Choose metrics to evaluate your experiment. - Next, __check in__ with your interviewer. If you’ve given a good answer, you may be done! If they ask follow-up questions, follow their lead. If you’re told that you’ve made a good guess but haven’t identified the right solution, go back to your hypotheses and choose the next most reasonable. Be sure to follow the interviewer’s prompts—they may be subtle. - If you’re done, don’t forget to __summarize and evaluate__ your approach. What would you have done differently, given more time / more data? ### Culture Fit In a third round, you’ll meet a team lead to assess how your skillset fits into the broader team and whether you’re a culture fit with Tinder. This round is primarily behavioral and conversational. Your interviewer wants to see that you are enthusiastic about Tinder and that your values align with Tinder’s. Instead of one-sided behavioral questions, expect to have a discussion covering: - Your career goals - Your working style and what drives you - How you like to collaborate with others - How you handle conflict - How you will be an asset to Tinder’s data science team To prep for this round, spend some time considering what drives you. How do your motivations align with [Tinder’s company vision and culture](https://www.lifeattinder.com/)? Below are common behavioral questions you might be asked: As you practice common behavioral questions, ask yourself: - What do I do to ensure that my actions align with my team and my company? - Why do I want to work at Tinder? What about Tinder’s vision resonates best with me? - How have I shown leadership in my past projects and personal relationships? - How have I been an effective communicator and collaborator? What has my experience taught me, and what will I bring to Tinder? There are no right answers to behavioral questions, but we recommend practicing to be succinct and straightforward. Interviews want authenticity and passion here, so don’t be shy! ## Tips and Strategies - __Emphasize real-world experience analyzing product data.__ Data science means vastly different things at different companies. Tinder’s focus is its mobile app product, so focus on experiences relevant to that mission and you’ll be well on your way to interview success. - __Read the job description carefully.__ Make sure you understand the scope of responsibilities and what Tinder is looking for for each open role. Check the list of technologies used at Tinder and tailor your responses to showcase your experience with these. - __Communication is key.__ Asking for clarification is always better than proceeding with incorrect assumptions. Interviewers are there to help; don’t hesitate to ask to repeat or reframe the question. - __Solve out loud.__ Create visuals—it’ll help you clarify your thoughts, and it’ll help your interviewer keep track of your process. Remember to answer all questions (both technical and behavioral) with the role in mind. Consider how your ideas and experience relate to what Tinder does. - __Be yourself.__ Recent Tinder hires stress that Tinder has a fun, welcoming culture and that you should feel safe to be yourself in the interview. No one expects perfection. Try to have fun with it.

Tinder

Interview Guides

Data Scientist

Interview Questions

Tell me about a time when you had to make a decision with a lot of uncertainty.

Tinder subscriptions renew monthly. Explain why different months may have different numbers of renewals.

Why do you want to work at Tinder?