below marketmost similar roles pay hereabove market
This role pays less than
64%
of similar roles. Most pay
$197,187–$254,750
— the blue band above.
At the midpoint, this role pays about
$214k
versus about
$226k
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
3544 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2734 roles with salary data.
The Senior Data Scientist, Apple Ads joins the Data Insights organization to help understand, measure, forecast, and optimize the advertising ecosystem across Apple Services. Working with Product, Engineering, Finance, and Sales teams, you will solve complex business challenges by analyzing large-scale datasets to influence product strategy and marketplace health. Your daily work involves designing experiments, defining measurement frameworks, and developing statistical, machine learning, forecasting, optimization, or econometric models. You will translate ambiguous business questions into structured analytical approaches to improve advertiser outcomes and operational decision-making. Required skills include proficiency in SQL and Python, a strong foundation in statistics, causal inference, and experimentation, and the ability to communicate technical findings to non-technical audiences. You will specifically address problems related to real-time bidding, attribution, and marketplace behavior.
What does a Data Scientist earn in California?
Median $214500 from 115 postings across 26 companies.
Analyze large-scale datasets to identify business opportunities and explain performance outcomes.
Design and evaluate experiments to measure the impact of products, features, and marketplace changes.
Develop statistical, machine learning, forecasting, optimization, or econometric models to solve business problems.
Define metrics and measurement frameworks to improve visibility into product and business performance.
Translate ambiguous business questions into structured analytical approaches and actionable recommendations.
Communicate complex analytical findings and data-driven insights to both technical and non-technical audiences.
Build scalable analytical frameworks and automation solutions to improve decision-making across the advertising ecosystem.
What we're looking for
Bachelor's degree in Statistics, Economics, Mathematics, Computer Science, Engineering, Data Science, or a related quantitative discipline, or equivalent practical experience.
Experience in Data Science, Analytics, Machine Learning, Quantitative Research, Business Analytics, or a related field.
Strong SQL skills and experience working with large-scale, complex datasets.
Strong Python programming skills with experience in common analytical libraries and scalable analytical workflows.
Strong foundation in statistics, experimentation, causal inference, and analytical problem solving.
Experience developing statistical, machine learning, econometric, forecasting, or optimization models to solve business problems.
Experience translating analytical findings into business recommendations and communicating with technical and non-technical stakeholders.
Masters or PhD in a quantitative discipline and experience in experiment design, causal inference, or advertising technology (preferred).
Data Science
A/B Experimentation
Causal Inference
Statistical Modeling
Machine Learning
Adaptive Control
Model-Predictive Control
Production Systems
Ads Optimization
Budget Allocation
Pacing Algorithms
Remote (Ontario, Canada)+328 days ago
$192,000–$202,500
Data Science
Causal Inference
Statistical Modeling
Machine Learning
Adaptive Control
Model-Predictive Control
Ads Optimization
Production Systems
Budget Allocation
Pacing Algorithms
Data Analysis
Python
SQL
Machine Learning
LLMs
Statistics
Causal Inference
Data Pipelines
Experimentation Design
Production Readiness
AI
Agentic Workflows
Data Quality