This role pays more than
90%
of similar roles. Most pay
$143,422–$219,650
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$182k
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
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
The Senior Data Scientist, Experimentation & Causal Inference joins the team to advance the scientific foundations of measurement and organizational learning across Apple Services. This role sits at the intersection of statistics, causal inference, and experimental design to define how success is measured and how evidence is gathered. You will build experimentation intelligence systems, develop advanced causal methodologies, and establish statistical standards for complex product behaviors and long-term user outcomes. Day-to-day responsibilities include designing experiments, developing measurement plans, performing meta-analysis, and ensuring experiment readiness. The role requires expertise in Python or R, power analysis, and identifying validity issues like sample ratio mismatch. Preferred skills include causal machine learning techniques such as uplift modeling, causal forests, and heterogeneous treatment effect estimation to evaluate AI-powered experiences and adaptive systems within a research-driven environment.
What does a Data Scientist earn in California?
Median $208978 from 66 postings across 27 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 standardized experimental designs 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 at scale.
Evaluate experiment validity by identifying issues like sample ratio mismatch, interference, and metric sensitivity.
Develop methodologies to evaluate AI-powered experiences and adaptive systems using causal machine learning.
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.
PhD in Statistics, Biostatistics, Economics, Computer Science, Data Science, 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.
Proficiency in Python and/or R programming languages.
Ability to evaluate experiment validity issues such as sample ratio mismatch, contamination, interference, and metric sensitivity.
Experience with modern causal machine learning methods like uplift modeling, causal forests, or double machine learning.
Strong communication skills to explain complex statistical concepts and causal claims to cross-functional teams.