Staff Data Scientist

Apple Inc

Quick summary

Work type: On-site
Location: Sunnyvale, CA
Salary: $203,300–$305,600 / yr
Posted: 42 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $161k

This role $254k

$74k most similar roles pay here $330k

This role pays more than 94% of similar roles. Most pay $126,800–$195,499 — the shaded band above. At the midpoint, this role pays about $254k versus about $161k 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 1723 open roles on FindRole.

Listed pay typically runs $162,500–$272,100 across 1398 roles with salary data.

Most-posted roles

View all roles at Apple Inc

At a glance

TL;DR · Staff Data Scientist

Apply Now Log in to save

As a Staff Data Scientist in the Corporate Functions team, you will lead the technical direction of AI-driven personalization across the Retail Online journey, partnering with engineers and business leaders to enhance customer experiences. Your responsibilities include architecting scalable data solutions, developing ML models for predictive analytics and causal inference, and integrating LLMs into core workflows to mitigate bias and hallucinations. You will also drive feature evaluation, shape product roadmaps, and establish best practices through mentorship and rigorous experimentation methodologies. The role requires expertise in statistical analysis, causal inference, and proficiency in Python or R, SQL, and Spark, alongside experience with LLMs and MLOps tools like Kubernetes and feature stores. This position addresses the challenge of scaling AI solutions to deliver meaningful customer impact at a large scale within the Retail Online ecosystem.

Skills

Python R SQL Spark LLMs RAG CausalML EconML GRF Kubernetes MLOps CI/CD Feature Stores PostgreSQL Docker Terraform AWS Azure GoogleCloud

What you'll do

Design and execute robust observational and experimental studies to advance causal inference methodologies.
Develop AI-native automated solutions using advanced statistical and machine learning methods.
Drive the evaluation philosophy for product features and proactively shape the product roadmap with insights.
Architect scalable data solutions and AI pipelines to deliver exploratory analyses, reports, and insights.
Integrate large language models into core data science workflows to mitigate bias and hallucinations.

What we're looking for

Masters in Statistics, Mathematics, Data Science, ML, Physics, Engineering, or CS
5+ years of experience as a Data Scientist
Expert proficiency in statistical analysis and causal inference methodologies
Proficiency in SQL, Spark, Python, or R for data modeling and analysis
Experience building solutions with LLMs and fine-tuning models
Excellent communication skills and product intuition

Similar roles

Staff Data Scientist

PayPal

Chicago, IL 8 days ago $142,210–$221,500

Python Pandas NumPy Scikit-learn Statsmodels SQL Statistical analysis Hypothesis testing Regression analysis Causal impact modeling Propensity Score Matching Difference-in-Differences Supervised learning Unsupervised learning Clustering Dimensionality reduction Hierarchical modeling Bayesian approaches Experimentation frameworks

Hybrid

Save

Staff Data Scientist - Experimentation

Apple Inc

San Francisco, CA 56 days ago $181,100–$318,400

Python SQL Pyspark Scala A/B testing Causal inference Regression analysis Propensity score matching Difference in differences Regression discontinuity Instrumental variables Data pipelines Data quality checks Monitoring systems Data lineage tracking CI/CD

Save

Staff Data Scientist - Experimentation

Apple Inc

New York, NY 56 days ago $181,100–$318,400

Python SQL Pyspark Scala R A/B testing Causal inference Regression analysis Propensity score matching Difference in differences Regression discontinuity Instrumental variables Data pipelines Data quality checks Monitoring systems Data lineage tracking CI/CD AWS Kubernetes

Save