TikTok Data Scientist Interview Guide (Updated for 2024)

TikTok Data Scientist Interview Guide (Updated for 2024)TikTok Data Scientist Interview Guide (Updated for 2024)


As TikTok continues to expand, the demand for skilled data scientists is increasing. This growth creates valuable opportunities for professionals interested in joining this innovative company.

This article is designed to provide a straightforward guide through the various stages of TikTok’s Data Scientist hiring process, including interviews, along with helpful tips.

So, if you’re an aspiring data scientist looking to secure a position at TikTok, this guide is tailored for you.

TikTok Data Scientist Interview Process

The interview process for a data scientist at TikTok thoroughly assesses a candidate’s technical abilities, problem-solving skills, and how well they fit with the company’s culture. It’s organized into different stages, each focusing on various aspects of a candidate’s skills and personality.

Let’s look through the process step by step:

Initial Application and Screening

The process usually begins with an online application or recruiter outreach, often via LinkedIn, followed by an initial HR interview. This stage assesses the candidate’s qualifications, background, and interest in the role. Applicants might also see status updates such as “written test” or “resume screening” on the application portal.

Technical Assessments

Candidates are typically subjected to technical tests early in the process, including SQL-based tests and LeetCode-style questions of medium difficulty. The first technical round often involves a HackerRank assignment or a live technical interview, focusing on data structures, algorithms, probability, and statistics. The second round includes a resume walkthrough and technical discussion with an engineer or data scientist, highlighting hands-on skills and experience.

Behavioral and Fit Assessment

This phase includes a behavioral interview to gauge the candidate’s alignment with TikTok’s culture and values. It’s followed by an interview with the prospective manager, focusing on behavioral aspects, team interaction, and long-term role aspirations.

Specialized and Final Rounds

Later stages might involve a take-home product analytics test, requiring concise solutions to real-world business scenarios. The final steps of the process include an in-depth HR interview to ensure cultural fit and a concluding interview with a department manager, focusing on the candidate’s understanding of TikTok’s mission and their potential contributions.

What are the Data Scientist Questions Commonly Asked at a TikTok Interview?

Data scientist interviews at TikTok are customized for each role and team, yet they consistently cover a standard set of topics. This approach ensures that all candidates are evaluated on the essential skills needed for the job, as shown in the graph above.

But to give you a clearer idea, here are the questions that are usually asked for a Data Scientist position over at TikTok.

1. Which model would you choose between one with 85% accuracy and one with 82% accuracy?

Question Category: Machine Learning

This question assesses your understanding of model evaluation beyond simple accuracy metrics. It’s being asked to gauge your ability to consider various factors that impact a model’s performance and suitability for a specific task.

How to Answer

When answering, it’s important to discuss the context of the models, such as the nature of the data, the problem being solved, and any trade-offs involved. Key considerations might include the models’ precision and recall, the balance of the dataset, and the cost of false positives/negatives in the specific application.


“In choosing between a model with 85% accuracy and one with 82% accuracy, I would first consider the context and the type of problem. For instance, if we’re dealing with a highly imbalanced dataset, a high accuracy might not be indicative of a good model. In such cases, I would look at other metrics like F1 score, precision, and recall. If the dataset is balanced and the cost of false positives and negatives is not significantly different, I might lean towards the model with 85% accuracy. However, if we’re in a scenario where false positives have a higher cost (like in medical diagnosis), I would prefer a model with a lower false positive rate, even if it has slightly lower overall accuracy. Additionally, I would consider the model’s complexity, scalability, and how it performs on unseen data to ensure we are not overfitting.”

2. How would you improve a classification model suffering from low precision in predicting customer purchases on an e-commerce platform?

Question Category: Machine Learning

This question targets your ability to diagnose and address specific issues in machine learning models, particularly regarding precision. Low precision in a classification model indicates a high number of false positives, which, in this context, means the model incorrectly predicts customer purchases.

How to Answer

When answering, consider strategies to reduce false positives and improve the model’s accuracy in identifying true positives. Key approaches could include revisiting the feature selection, adjusting the decision threshold, enhancing data quality, or trying different algorithms better suited for the data distribution.


“To address low precision in a classification model for predicting customer purchases, I would first analyze the features being used. Ensuring that the model includes relevant and discriminative features is crucial. If the feature set is too broad or not sufficiently informative, the model may struggle to differentiate between positive and negative classes accurately. I would also experiment with adjusting the decision threshold. A higher threshold can reduce false positives, though it’s important to balance this with recall. Another approach is to consider different modeling techniques, such as ensemble methods or algorithms that are less prone to overfitting. Regularization techniques can also help. Finally, if the data quality is suspect, improving data preprocessing and handling of outliers or missing values could significantly enhance model precision.”

3. How would you evaluate the success of advertising for an event based on a weekly 10% increase in search clicks?

Question Category: Machine Learning

This question examines your ability to interpret metrics in the context of advertising effectiveness. A weekly 10% increase in search clicks might initially seem positive, but deeper analysis is needed to fully understand its impact.

How to Answer

When evaluating the success of advertising, it’s important to look at both the absolute and relative changes in metrics and consider other factors like baseline traffic levels, market trends, and seasonal variations. Additionally, assessing how these clicks translate into actual engagement or conversions is crucial in determining the effectiveness of the advertising.


“To evaluate whether the 10% weekly increase in search clicks is a positive indicator of advertising success, I would first benchmark this increase against the baseline click rate before the advertising began. If the baseline was very low, a 10% increase might not be significant in absolute terms. I would also compare this growth rate to industry standards or past campaigns for similar events. It’s important to consider external factors like seasonality or concurrent events that might influence search behavior. Furthermore, I would analyze the conversion rate of these clicks - are more clicks leading to higher engagement or ticket sales for the event? If clicks are not converting, it could indicate that while the advertising is effective in attracting attention, it might not be reaching the right audience or conveying the message effectively. In this case, a review and adjustment of the advertising strategy, targeting, and content might be necessary.”

To get ready for machine learning interview questions, we recommend taking the machine learning course.

4. Is there a problem with an A/B test that shows one significant result out of 20 variants?

Question Category: Analytics and Experiments

This question examines your understanding of statistical significance in the context of multiple comparisons in A/B testing. When multiple variants are tested, the likelihood of finding at least one statistically significant result due to chance increases. This is known as the multiple comparisons problem.

How to Answer

In answering, discuss the importance of adjusting significance levels when testing multiple variants (e.g., using Bonferroni correction) and the potential risks of false positives in such scenarios.


“In an A/B test with 20 different variants, finding one variant as significant could be misleading due to the multiple comparisons problem. Each test increases the chance of observing a significant result purely by chance. To counter this, it’s important to adjust our threshold for significance. Using methods like the Bonferroni correction, where we divide the standard significance level (usually 0.05) by the number of tests (in this case, 20), would be appropriate. This means only considering results significant if the p-value is below 0.0025. Without such adjustments, we risk making a Type I error, falsely identifying a variant as significant when it’s not. Therefore, I would be cautious about claiming significance for one variant out of 20 without proper adjustment for multiple comparisons.”

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

Question Category: Analytics and Experiments

This question targets your ability to design an effective and scientifically robust A/B test, particularly in a digital marketing context. The aim is to determine the best combination of button color and position to maximize click-through rates.

How to Answer

When answering, it’s important to discuss the setup of control and variant groups, ensuring a statistically significant sample size, and the methodology for measuring and comparing results. The answer should reflect an understanding of experiment design, including randomization, controlling for external factors, and defining clear success metrics.


“To design an A/B test for optimizing button color and position, I would first establish a baseline with the current setup (red button at the top) as the control group. Then, I’d create variants with the button in blue, and in different positions (top and bottom). It’s crucial to have a control group for each position with the only change being the color to isolate the impact of color. Similarly, for testing position, the color should remain constant. Randomly assigning users to each group ensures that the sample is representative and reduces bias. The sample size needs to be large enough to detect differences in click-through rates with statistical significance. I’d track the click-through rates for each variant and compare them against the control groups. The variant with the highest statistically significant improvement in click-through rate would be considered the best option. Additionally, it’s important to run the test long enough to account for variability in user behavior but not so long that external factors could skew the results.”

6. How would you interpret a .04 p-value in an A/B test for a landing page conversion rate?

Question Category: Analytics and Experiments

This question assesses your understanding of p-values in the context of A/B testing. A p-value of 0.04 indicates the probability of obtaining the observed results, or more extreme, if the null hypothesis (no difference between the groups) is true.

How to Answer

When answering, it’s important to discuss the implications of this p-value in terms of statistical significance and the potential for making Type I or Type II errors. Also, the context of the business and the cost of making incorrect decisions should be considered.


“A p-value of 0.04 in an A/B test suggests that there is a 4% probability that the observed difference in conversion rates (or a more extreme difference) could have occurred under the null hypothesis, where we assume there is no real difference between the control and variant. Typically, a p-value threshold of 0.05 is used to determine statistical significance. Thus, a p-value of 0.04 would generally be considered significant, implying that the changes made on the landing page likely had a real effect on conversion rates. However, it’s important to consider the context. For instance, in high-stakes scenarios or where the cost of a false positive is high, we might want a more stringent threshold, like 0.01. Additionally, we should consider the practical significance of the result – even if statistically significant, the actual impact on the business needs to be evaluated to determine if the change is worth implementing.”

For practicing Analytics and Experiments, consider using the product metrics learning path and the data analytics learning path. These resources will help you understand and solve complex problems in this field.

7. Is there a problem with an A/B test having 20 different variants?

Question Category: Statistics and Probability

This question delves into the statistical challenges of conducting A/B tests with multiple variants. The core issue here is the increased risk of false positives – the more variants you test, the higher the chance of finding at least one significant result by chance. This is known as the problem of multiple comparisons or multiple testing.

How to Answer

When addressing this question, it’s important to discuss methods to control for this increased risk, such as using a Bonferroni correction or other statistical adjustments to maintain the overall Type I error rate.


“When conducting an A/B test with 20 different variants, the main concern is the risk of a false positive, or Type I error, due to multiple comparisons. The more tests we run, the higher the probability of incorrectly identifying at least one variant as significant. This can be mitigated by adjusting our significance threshold. One common method is the Bonferroni correction, which involves dividing the desired significance level (commonly 0.05) by the number of tests (20 in this case), setting a new, stricter threshold for each individual test. This means only considering a result significant if its p-value is below 0.0025. Without such adjustments, claiming that one variant is significant might be misleading, as this could easily occur by chance.”

8. What is the probability of finding a specific item on Amazon’s website, given its availability in different warehouses?

Question Category: Statistics and Probability

This question assesses your understanding of probability theory, particularly in applying it to real-world scenarios like inventory management. It’s a problem of calculating the combined probability of independent events – the item being in either warehouse.

How to Answer

The key is to understand how to calculate the probability of a union of two events, considering the overlap between them. This involves not just adding the individual probabilities, but also accounting for the joint probability of the item being in both warehouses.


“To calculate the probability of finding a specific item on Amazon’s website, given its availability in different warehouses, we need to consider the probabilities of the item being in each warehouse and the overlap between these probabilities. If the probability of the item being in warehouse A is 0.6 and in warehouse B is 0.8, the combined probability is not simply 0.6 + 0.8, as this would count the overlap twice. Instead, we use the formula: P(A or B) = P(A) + P(B) - P(A and B). Assuming the probabilities are independent, P(A and B) is P(A) * P(B), which is 0.6 * 0.8 = 0.48. Therefore, the combined probability is 0.6 + 0.8 - 0.48 = 0.92, or 92%.”

9. How would you explain the concept of a p-value to a non-technical person?

Question Category: Statistics and Probability

This question tests your ability to communicate complex statistical concepts in a simple, understandable manner, a crucial skill for data scientists who often need to explain their findings to non-technical stakeholders. A p-value can be a challenging concept to convey without statistical jargon.

How to Answer

Your explanation should focus on the idea of the p-value as a measure of how surprising the data is, assuming a certain hypothesis is true, without using overly technical language.


“To explain a p-value to a non-technical person, I would say: Imagine we have a theory or a guess about something, like whether a coin is fair or not. A p-value helps us understand how surprised we should be about what we observe if our initial guess was right. For example, if we think a coin is fair, but when we flip it 100 times, 70 of those flips are heads, a p-value tells us how unusual this is. A low p-value, like 0.01, means that what we observed would be very surprising if our initial guess (the coin is fair) were true. So, it suggests that maybe our guess was wrong. In other words, a low p-value indicates that the evidence we have is unusual under our initial assumption, and it might lead us to question or reconsider that assumption.”

For mastering Statistics and Probability, consider the Statistics and A/B testing learning path and the Probability learning path. These resources will provide you with a comprehensive understanding of the concepts and their applications.

10. Write a query to find the average quantity of each product purchased per transaction each year.

Question Category: Database

This question is designed to test your proficiency in SQL, particularly in using aggregate functions and handling date-related data. It’s a practical scenario in business analysis, where understanding patterns in sales or transactions over time is crucial. The challenge involves summarizing data at a yearly level while computing averages, which is a common requirement in data-driven decision-making processes.

How to Answer

When answering, you should focus on demonstrating your understanding of SQL functions like AVG() for calculating averages, YEAR() for extracting the year from dates, and GROUP BY for grouping data. It’s important to articulate your thought process in structuring the query, such as selecting the right columns, handling date data appropriately, and ensuring that your aggregations are logically sound. Emphasize how each part of the query contributes to the final result.


“To find the average quantity of each product purchased per transaction each year, I would start by selecting the product_id, extracting the year from the transaction date using the YEAR() function, and then calculating the average quantity using AVG(). These selections would be grouped by year and product_id using GROUP BY. The query ensures that the data is summarized on a yearly basis while providing insights into the average quantity sold per product, which is valuable for inventory planning and sales analysis.”

11. Write a query to get the total amount spent on each item by users that registered in 2022.

Question Category: Database

This question assesses your ability to combine data from multiple tables and perform conditional aggregations. It’s relevant in a retail or e-commerce context, where understanding customer spending patterns is key to business strategy. The query involves linking user information with their purchase history, focusing on a specific user segment (those registered in 2022).

How to Answer

In your response, highlight the importance of joining tables correctly using the JOIN clause and filtering data based on the registration date with a WHERE condition. Discuss the use of aggregate functions like SUM() to calculate total expenditures and the necessity of grouping the results by item. Your answer should reflect an understanding of how to efficiently extract meaningful insights from complex datasets.


“To find the total amount spent on each item by users registered in 2022, I would write a SQL query that joins the users and purchases tables on the user_id. I would then filter the results to include only those users who registered in 2022. Using the SUM() function, I’d calculate the total amount spent for each item, grouping the results by item_id. This query would provide valuable insights into the spending behavior of new users, which can inform marketing and sales strategies.”

12. Write a query to get the post-success rate for each day in January 2020.

Question Category: Database

This question challenges you to compute a specific metric, the post-success rate, from a dataset containing user actions. It’s a common type of analysis in social media analytics, where understanding user engagement and behavior is essential. The query requires you to calculate a ratio based on conditional counts, a task that involves both logical reasoning and technical SQL skills.

How to Answer

Your response should focus on the methodology to calculate the success rate, which is the ratio of posts submitted to posts entered. Discuss the use of conditional aggregation to count the different actions and how to correctly apply date filters to isolate the data for January 2020. Explain how grouping the data by day allows for a daily analysis of user engagement.


“To calculate the post success rate for each day in January 2020, I would write a SQL query that filters the events table for the actions post_enter and post_submit during the specified period. By using conditional aggregation, I would count the number of entries and submissions each day. The success rate for each day would then be calculated as the ratio of submissions to entries. This metric would provide insights into daily user engagement and the effectiveness of the platform’s features in encouraging users to complete their posts.”

To further enhance your knowledge of Databases, consider exploring the SQL learning path and the list of SQL questions and solutions in our interview questions database.

Tips When Preparing for a TikTok Data Scientist Interview

Now that you have some ideas on what’s going to be asked at a TikTok Interview for a DS role, here are some tips that can help you land it:

Understand the Interview Structure

Familiarize yourself with the multi-stage nature of TikTok’s interview process. Expect a mix of technical tests, behavioral interviews, and discussions around your experience and fit with the company culture. Knowing the structure will help you prepare for each stage effectively.

Consider checking out our TikTok Company Interview Guide to gain more insights into TikTok’s general interview process.

Technical Proficiency in SQL and Data Structures

Since SQL tests and LeetCode-style questions are common, focus on honing your SQL skills, especially in complex queries involving joins, aggregations, and window functions. Also, practice data structures and algorithms problems to be comfortable with medium-difficulty challenges.

You can try our Interview Questions to sharpen your SQL skills and practice data structures. This can help you focus on topics that are relevant to TikTok’s interview style.

Prepare for Practical Data Analysis Challenges

Expect scenarios where you’ll need to apply your data analysis skills in a practical context. Review key concepts in statistics, probability, and machine learning, and think about how you would apply these to real-world data.

Use our Data Science Learning Path to review key concepts in statistics, probability, and machine learning. You can also apply these concepts using the Takehomes feature to solve real-world data problems.

Focus on Communication Skills

Given the behavioral interviews and discussions with HR and potential future managers, it’s crucial to communicate effectively. Practice articulating your thoughts clearly, especially when explaining technical concepts or discussing your past experiences.

Through our Mock Interviews, you can enhance your communication skills, especially in explaining technical concepts. This will help you articulate your thoughts clearly during the interview.

Showcase Problem-Solving Abilities

Be prepared to demonstrate how you approach and solve problems. This could be through technical tests or during interviews where you’re asked to solve hypothetical problems or discuss past projects.

Participate in our challenges, where you can test your problem-solving skills. This will help you demonstrate your analytical abilities more effectively as you progress through your interview.

Practice Conciseness in Responses

Some rounds may require concise answers, like the take-home product analytics round with a 150-word limit. Practice being succinct yet thorough in your explanations.

Our takehome feature, which can help you practice summarizing your thoughts, might be particularly useful for this. Consider checking it out.

Be Prepared for Behavioral Assessments

Reflect on your past experiences and how they align with TikTok’s culture and values. Prepare to discuss your long-term career aspirations and how they fit with working at TikTok.

If you want to find other candidates who share their own experiences and align with TikTok’s culture, you can check out our interview experiences.

Review Your Resume Thoroughly

Be ready for a resume walkthrough, where you’ll need to discuss your technical skills and experiences in detail. Ensure you can confidently talk about every item on your resume.

Our coaching service, which ensures that you can confidently discuss every item on your resume, comes highly recommended for this. We can help you by providing expert guidance on how to effectively present your skills and experiences on your resume.


What is the Average Salary for a Data Scientist Role at TikTok?


Average Base Salary


Average Total Compensation

Min: $128K
Max: $242K
Base Salary
Median: $180K
Mean (Average): $178K
Data points: 16
Min: $204K
Max: $267K
Total Compensation
Median: $235K
Mean (Average): $235K
Data points: 2

View the full Data Scientist at Tiktok salary guide

The typical base salary for a Data Scientist position at TikTok, derived from 16 data points, stands at $177,813. When considering more recent salary information, this average increases slightly to $178,909.

The projected average for total compensation, based on 2 data points, is around $235,000, with a more recent average being $232,106.

Read our TikTok Data Scientist Salary Guide to know everything.

Which companies can I apply for as a Data Scientist aside from TikTok?

You can consider companies such as Google, Amazon, Airbnb, Microsoft, Tesla, and more if you’re looking to apply for Data Scientist roles in addition to TikTok. Really, there are no shortages of positions in this field, so it’s best to maintain a positive attitude and explore various companies that align with your interests and goals.

Check out our Company Interview Guide, where we cover interview guides similar to this one.

Are there any job postings about TikTok’s Data Scientist role here in InterviewQuery?

Yes, you can find TikTok Data Scientist jobs here on Interview Query. You can check them all out through our job board, where we post not only about TikTok’s openings but also about other companies as well.


Here at Interview Query, we aim to guide you so that you can secure your Data Scientist role at TikTok.

We believe that not only can you do this, but you can also learn other important aspects that you might find useful as you read through the content on our site.

If you ever feel like you’re still missing out on some important questions, then consider reading through our Data Science Interview Questions guide, located in our blog section that houses various other topics that can be of use to you as well.

So, in conclusion, by focusing on these areas, you can better prepare for the various stages of the TikTok Data Scientist interview process, showcasing your technical abilities, problem-solving skills, and cultural fit with the company.