Senior Data Scientist, Experimentation & Causal Inference

Apple Inc.; Culver City (CA); On-site; USD 175,000 - 309,000; Full time

Job summary

Apple Inc. in Culver City, CA, seeks a Senior Data Scientist, Experimentation & Causal Inference to advance measurement, experimentation, and organizational learning across Apple Services. You will design measurement frameworks, experiments, and causal evidence pipelines.

You will collaborate across product, growth, engineering, data engineering, and strategic science teams to build scalable experimentation intelligence systems and advance causal methodologies for long-term user outcomes.

Qualifications

• Master's degree or higher in statistics, data science or related quantitative field.
• 5+ years designing, analyzing, and interpreting large-scale experiments or causal analyses.
• Deep expertise in experimental design, statistical inference, and causal inference.
• Experience developing measurement plans, KPI frameworks, guardrails, success criteria.
• Strong programming skills in Python and/or R.

Responsibilities

• Own components of the experimentation science ecosystem across teams.
• Define measurement frameworks, experiment methodologies, and causal approaches.
• Develop advanced causal methodologies and experimentation intelligence systems.
• Drive cross-experiment learning initiatives and governance.
• Communicate complex statistical concepts and causal claims effectively.
• Collaborate with product, growth, engineering, and data teams.
• Establish standards for measurement and experimentation at scale.

Skills

Statistical reasoning

Experiment design

Causal inference

Communication

Education

Master's degree or higher in Statistics, Data Science, Biostatistics, CS, Economics, or related quantitative discipline

PhD in Statistics, Biostatistics, Economics, CS, Data Science, or related field

Tools

Python

R

Job description

Culver City, California, United States Machine Learning and AI

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.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
• Cross-Functional Collaboration
• Communication & Influence

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.

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 $175,000 and $308,500, 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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

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Apple accepts applications to this posting on an ongoing basis.