Data Scientist, Experimentation & Causal Inference

At Apple, some of the most important decisions are shaped by the quality of the evidence behind them. We are seeking a Senior Data Scientist, Experimentation & Causal Inference to help advance the scientific foundations of measurement, experimentation, and organizational learning across Apple Services.\n\nThis role sits at the intersection of statistics, causal inference, experimental design, and decision-making. You will help define how success is measured, how experiments aredesigned, and how causal evidence is generated and accumulated across the organization.\n\nBeyond individual experiments, you will help build the next generation of experimentation intelligence by transforming isolated experiment outcomes into reusable scientific knowledge. As Apple expands investments in AI-powered experiences and intelligent systems, this role will also help evolve the experimentation methodologies used to evaluate increasingly complex product behaviors and long-term user outcomes.\n\nThe ideal candidate combines deep statistical expertise with strong scientific curiosity and a passion for developing rigorous methodologies that improve how organizations learn and make decisions at scale., 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.\n\nThis 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.\n\nThis 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.\n\nThe 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.

Master’s degree or higher in Statistics, Data Science, Biostatistics, Computer Science,Economics, Applied Mathematics, Operations Research, or a related quantitative discipline.\n5+ years of experience designing, analyzing, and interpreting large-scale experiments or causal analyses.\nDeep expertise in experimental design, statistical inference, causal inference, power analysis, and measurement strategy.\nExperience developing measurement plans, KPI frameworks, guardrails, success criteria, and experiment readiness processes.\nStrong programming skills in Python and/or R.\nAbility to evaluate experiment validity issues such as sample ratio mismatch, contamination, interference, instrumentation errors, metric sensitivity, and under powered designs.\nStrong communication skills with the ability to explain complex statistical concepts andcausal claims.

PhD in Statistics, Biostatistics, Economics, Computer Science, Data Science, Applied Mathematics, Operations Research, or a related quantitative discipline.\nExperience with modern causal machine learning methods such as uplift modeling, causal forests, heterogeneous treatment effect estimation, Bayesian experimentation, double machine learning, or related methodologies.\nExperience conducting meta-analysis, cross-experiment synthesis, transferability analysis, or experimentation intelligence programs.\nExperience building experimentation standards, measurement governance, experimentation intelligence repositories, or causal learning systems at scale.\nExperience evaluating machine learning systems, recommendation systems, adaptive products, or AI-powered experiences using experimentation and causal inference methodologies.\nPublications or research contributions in venues such as KDD, CIKM, WWW, WSDM, ICML, NeurIPS, AISTATS, JSM, or related conferences and journals.\nExperience operating in highly technical, research-driven, or large-scale product experimentation environments.