Data Scientist

Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day.

We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X,  and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process.

Join the Monetization team at Twitch, where we build the products that help creators make a living on the platform. You'll work on products like Subscriptions, Bits, and Gifting, and the pricing and packaging decisions behind them. 

You'll partner closely with product, engineering, finance, and data teams to measure the impact of new features, design and analyze experiments, and apply causal inference methods to inform decisions where A/B testing isn't possible. The work ranges from high-velocity experimentation on consumer-facing products to deeper pricing, policy, and segmentation analyses where causal identification is the central challenge.

This role is well-suited for someone with a strong economics or causal ML foundation who wants to apply rigorous statistical thinking to real product decisions at scale. You'll need to be comfortable writing SQL, working with imperfect data, and partnering with stakeholders to turn analysis into product impact. Our team is based at Twitch HQ in San Francisco, CA.

You can work in San Francisco, CA; New York, NY; or Seattle, WA 

• Apply causal inference methods where experimentation isn't feasible
• Develop models and analyses that inform pricing, segmentation, and revenue optimization
• Design, run, and analyze A/B experiments 
• Partner with product, engineering, and finance to translate ambiguous business questions into measurement frameworks
• Build and maintain dashboards, reporting, and analytical tooling that support ongoing decision-making

• 3+ years of experience as a data scientist, applied scientist, economist, or related field; OR a PhD in Economics, Statistics, Computer Science, or related quantitative field
• Proficiency in SQL
• Proficiency with Python or R
• Strong foundation in experimentation and causal inference, including A/B test design and quasi-experimental methods
• Strong communication skills across technical and non-technical stakeholders
• Comfort building dashboards and recurring reporting

• Master's or PhD in Economics, Statistics, or a related quantitative field
• Industry experience working on consumer products with high transaction volume (subscriptions, marketplaces, payments) 
• Hands-on experience with Airflow, SageMaker, or deploying ML models in production
• Familiarity with modern causal ML methods (double ML, causal forests, heterogeneous treatment effects)