Senior Data Scientist, Experimentation & Causal Inference

As a Senior Data Scientist, Experimentation & Causal Inference, you will own key components of the experimentation science ecosystem. You will work across product, growth, engineering, data engineering, and strategic science teams to define measurement frameworks, experiment methodologies, statistical standards, and causal inference approaches that improve organizational decision quality. This role extends well beyond traditional A/B testing. You will help establish experimentation standards, develop advanced causal methodologies, build experimentation intelligence systems, and drive cross-experiment learning initiatives. You will play a critical role in ensuring that experimentation generates reliable evidence, scalable insights, and reusable scientific knowledge. This includes helping establish experimentation approaches for emerging product paradigms where user interactions, adaptive systems, and long-term outcomes introduce new measurement and causal inference challenges. The ideal candidate possesses strong expertise in experimental design, causal inference, statistical modeling, and scientific reasoning. Experience with modern causal machine learning techniques, heterogeneous treatment effect estimation, meta-analysis, and experimentation intelligence systems is highly desirable. Minimum Qualifications Master's degree or higher in Statistics, Data Science, Biostatistics, Computer Science,Economics, Applied Mathematics, Operations Research, or a related quantitative discipline. 5+ years of experience designing, analyzing, and interpreting large-scale experiments or causal analyses. Deep expertise in experimental design, statistical inference, causal inference, power analysis, and measurement strategy. Experience developing measurement plans, KPI frameworks, guardrails, success criteria, and experiment readiness processes. Strong programming skills in Python and/or R. Ability to evaluate experiment validity issues such as sample ratio mismatch, contamination, interference, instrumentation errors, metric sensitivity, and under powered designs. Strong communication skills with the ability to explain complex statistical concepts andcausal claims. Preferred Qualifications PhD in Statistics, Biostatistics, Economics, Computer Science, Data Science, Applied Mathematics, Operations Research, or a related quantitative discipline. Experience with modern causal machine learning methods such as uplift modeling, causal forests, heterogeneous treatment effect estimation, Bayesian experimentation, double machine learning, or related methodologies. Experience conducting meta-analysis, cross-experiment synthesis, transferability analysis, or experimentation intelligence programs. Experience building experimentation standards, measurement governance, experimentation intelligence repositories, or causal learning systems at scale. Experience evaluating machine learning systems, recommendation systems, adaptive products, or AI-powered experiences using experimentation and causal inference methodologies. Publications or research contributions in venues such as KDD, CIKM, WWW, WSDM, ICML, NeurIPS, AISTATS, JSM, or related conferences and journals. Experience operating in highly technical, research-driven, or large-scale product experimentation environments.