Twitch Data Scientist Interview Questions + Guide in 2025

Twitch is a live video streaming platform that allows users to watch and broadcast live-streamed or pre-recorded videos of the broadcaster’s video game gameplay. The platform is owned and operated by Twitch Interactive, a subsidiary of Amazon. Founded in 2011 as an offspring of the “stream anything platform”, Justin.tv, its prime focus is on streaming live video games, including broadcasting Twitch’s own hosted eSports competitions. Besides these functions, Twitch also broadcasts music and creative content, which can be viewed live on the site or from “Video on Demand”.

As of 2024, Twitch has over 140 million monthly active users and over 7.30 million people stream every month.

With the amount of data generated from different daily streams, Twitch’s data science team performs a wide range of analysis to help shape product decisions. This feat is achieved through enhanced data pipelines “that collects data, cleans data, and loads over a billion events per day into their data warehouse.”

Twitch has a “science team”, consisting of titles and roles related to data science. It is supported by three pillars, namely “data science research, user experience research, and data governance.” Data science sits right in the middle of these three science organizations and collaborates with the other teams on many occasions.

Data scientists’ roles at Twitch are greatly influenced by the teams they are working with, and, as such, the roles and functions may range from product-focused analytics to machine learning and deep learning algorithms. Currently, there are two main types of data scientists: the “product strategy-oriented data scientists”, who provide business-impact insights from data analysis, and the “data product data scientists”, who build specific algorithms and techniques yield new products informed by data.

Required Skills

Twitch only hires qualified data scientists with a minimum of 3 years (5 years plus for senior data scientist roles) industry experience in data science-related projects.

Note: applications are processed and evaluated based on specific industry experience related to the job roles on the teams.

Other relevant qualifications include:

• Advanced degree (MS or Ph.D.) in Computer Science, Mathematics, Statistics, or related fields.
• Experience in applying supervised and unsupervised algorithms to large-scale data.
• Sound background in data-science fundamentals: data manipulation in R or Python, SQL, and statistics (Hypothesis testing, Regression, etc.).
• Advanced knowledge of machine-learning techniques applied to large-scale data.
• Experience with building data pipelines, data warehouses, dimensional modelling, building aggregates, and optimizing data workstreams from data preparation to analysis to deployment.
• Sound background in AWS.
• Experience with database management system software, e.g., MySQL, PostgreSQL, etc.
• Experience designing and assessing the impact of A/B experiments in a product development cycle.
• High level of comfort with creating dashboards in Tableau or similar software.
• Experience in data analysis and communication around data, including experimentation, data visualization, and defining KPI strategy for business.
• Experience with programming languages like R, Python, C/C++, Go, or Java.
• Product analytics experience with signup funnels, engagement metrics, and retention analysis.
• Proficiency in SQL and ETL/ELT in a business environment with complex data sets.

Although Twitch has a dedicated “Science Team” consisting of data scientists, data analysts, and data engineers, data scientists are often embedded within other teams and sometimes collaborate with other departments. As a large company with data scientists working in over 20 teams, on the individual level, roles at Twitch are inherently tied to specific teams.

Based on the team’s needs, data scientist roles at Twitch may include:

• Advertising Product: leveraging a long list of advanced data analytics tools and methodology to work on a wide range of challenging problems including econometric modelling and auction dynamics, pricing and segmentation, and maximizing value for viewers and advertisers.
• Core Product: setting up and tracking KPIs, designing experiments, evaluating A/B tests, and implementing data instrumentation for developing strategy and evaluating/improving product plans.
• Alliances: defining and tracking KPIs, supporting strategic initiatives, evaluating new lines of business, and helping shape the way performance is measured with Twitch’s deals.
• Financial Planning and Analysis: overseeing data instrumentation, design dashboard/report building, and metrics reviewing to guide financial decisions and provide business-impact insights.
• Marketing: leading analysis and optimization of event marketing programs. Working cross-functionally with go-to-market roles, including growth, product, content events, and brand marketing teams.
• Mobile: developing strategy and evaluating product initiatives within the Mobile team through defining KPIs, designing experiments, evaluating A/B testings, and supporting strategic initiatives.
• Core Science: driving evidence-based decision-making throughout Twitch with data analytics and machine learning models. Collaborating with specialists in data science, analytics, engineering, and economics disciplines to effectively develop reliable and reproducible analyses at scale.

Tips for Twitch Interviews

Twitch’s Data Scientist interview is a combination of data science concepts standardized to assess an applicant’s ability to apply statistical and analytics concepts to understanding and predicting user behavior and answering business questions based on the analysis.

It helps to brush up on your knowledge of:

• statistics and probability
• time series analysis
• experimental designs
• A/B testing
• predictive modelling concepts

It also helps to know the metrics used at Twitch, especially those related to products and features.

Twitch offers an ecosystem that allows employees to thrive and be the best version of themselves by encouraging them to get their hands dirty and find something they love. A lot of emphasis is placed on building high-performing teams through mentorship programs, and in fact, the ability and desire to mentor is something Twitch looks for in applicants.