About the Role We are looking for a Senior Data Scientist - Experimentation & Causal Inference to serve as the statistical brain behind our rapidly growing experimentation program. You will NOT be building infrastructure or writing backend services — our platform engineering team handles that. Your job is to design, validate, and continuously improve the statistical frameworks that sit on top of the platform. You will join a team of data scientists and platform engineers within the Big Data group, reporting to the Head of Big Data. The experimentation platform engineering team implements your specifications into production systems.
Continuous A/A Experiment Monitoring Design and run continuous A/A experiments as an always-on health check for the data pipeline (from client-side event reporting through message queues to the real-time data warehouse). Monitor metric baseline volatility and pipeline stability, ensuring the instrumentation layer remains trustworthy over time. the platform team implements the monitoring infrastructure.
Education: MS or PhD in Statistics, Biostatistics, Economics (Econometrics), Computer Science, or a related quantitative field. Experience: 3+ years of industry experience designing and analyzing online controlled experiments at a tech company with meaningful user scale (not exclusively survey experiments or clinical trials). Solid foundations in hypothesis testing, power analysis, multiple testing correction, and sequential testing. Hands-on experience with at least two of: variance reduction methods (CUPED or similar), sample ratio mismatch detection, or continuous data quality monitoring for experiments. Programming: Proficient in Python (scipy, statsmodels, or equivalent) for statistical analysis and simulation. Comfortable writing complex SQL (window functions, CTEs) for data extraction and validation. AI-Native Workflow: Strong AI Sense — extensive hands-on experience with AI coding tools (Claude Code, OpenClaw, or similar). Demonstrated ability to leverage AI assistants to automate repetitive analytical tasks, accelerate code development, and build self-serve tooling faster. You treat AI tools as a daily productivity multiplier, not a novelty. Understanding of real-time streaming data pipelines (Kafka, Flink) — not as an engineer, but how data flow affects statistical validity.
Senior Data Scientist - Experimentation & Causal Inference in washington at Unknown Company
This position is listed as full time and onsite.