Sr. Research Data Scientist

Roku•San Jose, MA

1d•Hybrid

About The Position

Roku is seeking a Senior Research Data Scientist to join its Data Science team. This role will focus on leading the development of a causal inference platform to measure and optimize the incremental impact of customer actions, product features, and business interventions on long-term outcomes. The position involves building methods and systems to make high-confidence decisions from observational data, owning the full lifecycle of causal measurement, and communicating findings to senior leadership. This role is ideal for an applied economist or econometrician skilled in rigorous research and production engineering, comfortable with large-scale data analysis.

Requirements

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.

Responsibilities

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.