Senior 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 u0026 Causal Inference to help advance the scientific foundations of measurement experimentation and organizational learning across Apple 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 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 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 u0026 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 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 includes helping establish experimentation approaches for emerging product paradigms where user interactions adaptive systems and long-term outcomes introduce new measurement and causal inference 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.

Experiment Design u0026 Measurement StrategynScientific Experiment DesignnExperiment Readiness u0026 Statistical GovernancenCausal Inference u0026 Methodology DevelopmentnAdvanced Causal ModelingnExperimentation Intelligence u0026 Meta-AnalysisnCross-Experiment Learning SystemsnCross-Functional CollaborationnCommunication u0026 Influence

Masters degree or higher in Statistics Data Science Biostatistics Computer ScienceEconomics Applied Mathematics Operations Research or a related quantitative discipline.n5 years of experience designing analyzing and interpreting large-scale experiments or causal expertise in experimental design statistical inference causal inference power analysis and measurement developing measurement plans KPI frameworks guardrails success criteria and experiment readiness programming skills in Python and/or to evaluate experiment validity issues such as sample ratio mismatch contamination interference instrumentation errors metric sensitivity and under powered 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 with modern causal machine learning methods such as uplift modeling causal forests heterogeneous treatment effect estimation Bayesian experimentation double machine learning or related conducting meta-analysis cross-experiment synthesis transferability analysis or experimentation intelligence building experimentation standards measurement governance experimentation intelligence repositories or causal learning systems at evaluating machine learning systems recommendation systems adaptive products or AI-powered experiences using experimentation and causal inference or research contributions in venues such as KDD CIKM WWW WSDM ICML NeurIPS AISTATS JSM or related conferences and operating in highly technical research-driven or large-scale product experimentation environments