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 & Causal Inference to help advance the scientific foundations of measurement, experimentation, and organizational learning across Apple Services.
This 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.
Beyond 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.
The 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.
Description
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.
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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.
Responsibilities:
Experiment Design & Measurement Strategy
Scientific Experiment Design
Experiment Readiness & Statistical Governance
Causal Inference & Methodology Development
Advanced Causal Modeling
Experimentation Intelligence & Meta-Analysis
Cross-Experiment Learning Systems
Cross-Functional Collaboration
Communication & Influence
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.
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.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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