ByteDance Ltd. is a Chinese multinational tech company that developed the Toutiao ("Headlines") content platform, formerly a news recommendation engine, and TikTok, the video-sharing social network service. Over the years, Tiktok has soared in popularity and, according to a 2020 report, the app currently has over 800 million active users worldwide with over two billion downloads globally on app stores and Google Play. One important feature of TikTok is its “machine learning-backed recommendation engine," which is a hot topic within the data science community.

A unique attribute of the TikTok recommendation system is its user-centric design. This advanced AI algorithm collects data from users and uses it to build a workflow of recommendations tailored specifically to users’ preferences, which starts from a cold-start adjustment and then gradually builds up to an explicit recommendation for active users.

The Data Scientist Role at ByteDance

ByteDance Data Scientists have high qualifications to work on products, such as Tiktok.
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ByteDance is shaping the way AI changes our lives, and data scientists are at the forefront of this journey. Data scientists, data engineers, and data analysts form the core data structure at this company and are responsible for bridging the gap between data and sound business decision-making. Data scientist roles at ByteDance can range from basic data analytics methods, such as data analysis, hypothesis testing, A/B testing, experimentation, data visualization, and presentation, etc., to more advanced machine learning and deep learning techniques, such as regression, classifications, clustering, etc.

Required Skills

As a company that prides itself as a leading body in AI technology, ByteDance takes only the best.  Candidates applying for the data scientist role should possess at least three years of industry experience doing quantitative analysis or any other data analytics related role.

Other basic requirements include:

  • Bachelor's or Master's degree in Computer Science, Information Systems, Electrical Engineering, Physics, Mathematics, Statistics, Economics, or other quantitative fields.
  • Hands-on experience with SQL, ETL, or at least with one of the scripting languages e.g. Python, Java, R, Scala, Go, C++.
  • Experience with statistical software such as R, MATLAB, Python, pandas, Tableau, etc.
  • Experience with statistical data analysis such as linear models, multivariate analysis, stochastic models, sampling methods, hypothesis testing, model evaluation (regression, decision trees, k-means, etc.), and common regression and classification algorithms.
  • Hands-on experience handling large datasets and analytical tools such as Hive, Spark, etc.
  • Experience with predictive analytics, statistics, and machine learning techniques and algorithms.
  • Experience with a data visualization tool (e.g. Mode Analytics/Chartio/Tableau).
Interested in learning more about the qualifications of data scientists at top companies? Check out the Zoom, Robinhood, and IBM Data Scientist Interview Guides.

Data Scientist Teams at ByteDance

Data scientists at ByteDance collaborate heavily to improve ByteDance products and services.
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ByteDance boasts a wide range of products and content services across a wide array of languages and cultures and, as such, possesses a large number of departments and teams. Data scientists work within the data science team and cross-functionally with other internal teams on new and existing ByteDance products and improving user experiences.

Depending on the assigned team, specific roles can vary and may be tailored specifically to particular projects.

Listed below are some of the data science teams at ByteDance and specific data scientist roles within that team:

  • Business Platform: Building business service platforms for core internal business, providing business/technical solutions in critical areas, working with large and complex data to solve difficult, non-routine analysis problems, and applying advanced analytics methods to provide insight from data. Other responsibilities can involve developing and conducting analysis, forecasting, and optimization methods to improve Bytedance core products such as payment, game, and growth.
  • Game Strategy: Conducting analysis, including building and managing Business Intelligence (BI) reporting to monitor business growth and designing models to predict growth trends, and collaborating with PMO to design and implement project management (PM) processes to enforce data-driven decisions.
  • Research and Design (R&D) Networking: Developing scalable data pipeline for collection and aggregation of network system data, such as network resource usage, traffic statistics, device operation status, etc., and making this data universally available for both real-time and retrospective analysis use. Roles also involve developing analytical models for extracting insights from network events and making recommendations based on results from such analysis.
  • Global Security Architecture: Focusing on corporate security, risk, and privacy technical strategy and research prioritization, applying security analytics using big data, machine learning, and artificial intelligence (AI), working with cutting-edge cyber security technology to develop models that will recognize patterns, changes, deviations, and hidden insights, to drive defense decisions and strategy against advanced cyber threats. Other basic roles in this team include developing robust data collection processes, ensuring data quality and integrity, building predictive models, and interpreting data analysis results.
  • R&D Machine Learning: Developing tools and processes to analyze from ByteDance database, developing an efficient and robust A/B testing framework,  building custom models and algorithms to apply to data sets, testing model quality and performance, and utilizing predictive modeling to optimize customer experiences, generate revenue, and drive growth. Functions also include teaming up with internal teams, especially engineers to implement models, monitor results, and design learning solutions.
  • Analytics, Trust & Safety (TikTok): Leveraging data to identify trends, conducting root cause analysis for insights, developing metrics to evaluate risks, developing dashboards for risk detection, and conveying the results across multi-level organizations and stakeholders through data-driven recommendations.
  • TikTok Ads Platform: Building and prototyping analysis pipelines to provide scalable insights, developing metrics, and leveraging TikTok’s large and complex data to solve difficult and non-routine problems.
  • User Growth (TikTok): Building and analyzing dashboards and reports, evaluating and defining metrics, managing performance marketing models, partnering with engineering teams on areas of scalability and performance optimization, and collaborating with stakeholders to understand business problems.

Interview Process

ByteDance Data Science Interviews consist of multiple stages, including a screen and onsite interviews.
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ByteDance has a pretty standard hiring structure, starting with one or two phone screens (usually with an HR or a member of your future team). After these sessions comes the onsite interview, which can take place either in one of ByteDance’s physical locations, or through Zoom in remote cases. Onsite interview stages generally range between two to six back-to-back interview sessions, depending on the level/seniority of the role.

Initial Screen

Like most technical role evaluations, this interview is designed to be straight-forward and exploratory. Conversation in this interview will revolve around past project experiences that align with the position and skills mentioned in your resume that may prove useful to the role. This interview may get technical with domain experience and case study questions, so it is best to prepare for technical questions on the off-chance that you are asked to answer them.

Sample Questions

  • What did you do in your last project and what was the innovation?
  • What’s your favorite product?

Technical Screen

This is a one hour long interview with a data engineer. Questions span across behavioral, algorithm concepts, and data structure domains.

Onsite Interview

This is the last interview stage in the ByteDance Data Scientist hiring process. Questions in this round are structured around data science concepts, including algorithms, machine learning theories, and data structure.

The onsite interview process can be broadly described below:

  • Behavioral round with a product manager: questions are usually about product management.
  • Technical and coding round: mainly machine learning, basic statistics, and some SQL queries.
  • Leadership interview with a team manager.

Notes and Tips

The ByteDance Data Scientist interview features standardized questions tailored to specific roles, and the scope of the interview covers the length and breadth of data science, as well as leadership quality and product sense.

An important tip is to read up on the company mission and its six core values. Structure your answers and responses around one or more core values, especially when you are talking about yourself and the projects you’ve done in the past.

Common questions will be tied around statistics, especially probability (both joint and conditional), central limit theorem, distributions (including exponential, geometric, and binomial), etc., experimental design, SQL, and some machine learning techniques, such as regression, classification algorithms, clustering, etc.

A strong foundation in statistics and probability, A/B testing, experimentation, etc. is necessary to ace the ByteDance Data Scientist interview, so remember to brush up on your knowledge of these topics.

For more technical aspects, visit and practice lots of ByteDance Data Scientist questions.

ByteDance Data Scientist Interview Questions

  • If you were the data manager for a product and responsible for providing ten metrics to the CEO every day, what metrics would you choose?
  • A company has introduced a new feature for a product for a month. How do you evaluate the feature?
  • What single metric would you use to measure how well a new app is doing?
  • How do you make a trade-off between quality and cost?
  • Tell me about a time when you relentlessly worked on improving the quality of an already working product or service.