This role pays more than
85%
of similar roles. Most pay
$140,000–$220,100
— the shaded band above.
At the midpoint, this role pays about
$242k
versus about
$180k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3513 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2730 roles with salary data.
The Senior Data Scientist, Experimentation & Causal Inference joins the team to advance the scientific foundations of measurement, experimentation, and organizational learning across Apple Services. This role sits at the intersection of statistics, causal inference, and experimental design to improve decision quality through robust evidence. You will build next-generation experimentation intelligence by transforming isolated outcomes into reusable knowledge and developing advanced causal methodologies for complex product behaviors. Key responsibilities include designing experiments, establishing measurement frameworks, performing meta-analysis, and managing statistical governance. The role requires expertise in Python or R, power analysis, and identifying issues like sample ratio mismatch or interference. Preferred skills include modern causal machine learning techniques such as uplift modeling, causal forests, and heterogeneous treatment effect estimation to evaluate AI-powered experiences, recommendation systems, and adaptive products within a research-driven environment.
What does a Data Scientist earn in California?
Median $211329 from 80 postings across 29 companies.
Design and implement measurement frameworks, KPI systems, and success criteria for product experiments.
Develop advanced causal inference methodologies and models to evaluate complex product behaviors.
Establish experimentation standards and statistical governance across the organization.
Build experimentation intelligence systems to transform isolated results into reusable scientific knowledge.
Conduct meta-analysis and cross-experiment synthesis to drive organizational learning.
Evaluate experiment validity by identifying issues like sample ratio mismatch, interference, and metric sensitivity.
Develop methodologies to evaluate AI-powered experiences and adaptive systems with long-term outcomes.
What we're looking for
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.
Ability to evaluate experiment validity issues such as sample ratio mismatch, contamination, interference, instrumentation errors, metric sensitivity, and under powered designs.
Strong communication skills to explain complex statistical concepts and causal claims.
PhD in a quantitative field, experience with modern causal machine learning, meta-analysis, or research publications (preferred).