Senior Research Data Scientist

Our Data Science team is a high-impact research team actively shaping the future of TV, using Big Data to build and enhance the user experience on the Roku streaming platform. Our production-ready machine learning models and statistical solutions optimize the user experience across all of Roku's core business models and products, and our scientists engage closely with business, product, and engineering leaders to make material and measurable impacts on the success and growth of the platform.

As a Senior Research Data Scientist on Roku's Data Science team, you will lead the development of a best-in-class causal inference platform that measures and optimizes the true incremental impact of customer actions, product features, and business interventions on long-term outcomes. Partnering with the Customer Growth organization, you will build the methods and systems that enable Roku to make high-confidence decisions from observational data when randomized experiments are not feasible.

You will own the full lifecycle of causal measurement-from gathering business requirements and defining estimation approaches, to partnering with Engineering to productionize scalable causal pipelines and communicating findings to senior leadership. Your work will directly inform growth, retention, and monetization strategy across the platform, making this role ideal for an applied economist or econometrician who excels at the intersection of rigorous research and production engineering.

This is someone equally comfortable deriving identification strategies and building estimators on terabyte-scale data.

For California, New York, and Massachusetts only - The estimated annual base salary for this position is between $330,000- $375,000 annually.

Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.

At Roku, we don't just use AI, we work with it. AI agents and smart tools help power drafts, analysis, and repetitive workflows, while our people bring direction, judgment, and accountability. We're looking for curious, adaptable builders who can show how they've used AI or automation to move faster, raise the bar, and scale their impact.

We value your AI skills if you have built fluency across the agentic engineering toolchain - coding harnesses like Claude Code or Cursor, MCP servers, custom skills, or agent frameworks. And you can describe projects where you shipped real work with these tools. You know how to drive an agent, verify its output, and ramp on an unfamiliar codebase with an agent helping you.

- Design, build, and productionize a causal inference platform that standardizes how Roku measures the incremental impact of customer actions and business decisions

- Research and implement causal estimation methods, including heterogeneous treatment effects, tailored to Roku's data and business questions

- Build long-term outcome frameworks that enable impact projection from limited observation windows

- Develop diagnostic and validation standards at scale to ensure credibility of causal estimates

- Leverage AI to create counterfactual scenarios and build tools that help users run, understand, and act on causal estimates correctly

- Work cross-functionally with Data Engineering, Product Management, and Core Analytics to translate business questions into well-defined causal problems and deploy production-ready solutions

- Contribute to the technical vision of the Data Science team and the broader research agenda across causal inference, predictive modeling, and experimentation

- PhD in Economics, Econometrics, Statistics, or a closely related quantitative field with a strong emphasis on causal inference

- 10+ years of experience applying causal inference and machine learning methods to real-world problems, with a demonstrated track record of measurable impact

- Deep expertise in observational causal methods such as propensity score matching, Double Machine Learning, doubly robust estimation, instrumental variables, and difference-in-differences

- Experience building reusable causal inference tools or platforms beyond one-off analyses

- Proficiency with Spark, Ray, SQL, Python, and ML frameworks such as scikit-learn, XGBoost, and LightGBM

- Experience with terabyte- or petabyte-scale datasets in distributed computing environments

- Strong communication skills with the ability to translate econometric findings into clear business recommendations

- Technology industry experience; connected TV, streaming, or advertising experience is a plus