1,570 jobs for "Business Analytics Lead"

Would you like to help shape the future of Apple's products and platforms while impacting some of the most widely used software in the world? The Apple Services Engineering (ASE) organization drives innovation behind Apple's cloud-based experiences for over a billion global users - including iCloud+, Photos, Mail, Messages, FaceTime, Calendar, and Notes. We build the infrastructure and intelligence that enable Apple's ecosystem to deliver seamless, privacy-first experiences every day.

The ASE Data Organization plays a central role in this mission by embedding data-informed thinking into every decision. Partnering with product, engineering, and business teams, we combine analytical rigor, product acumen, and technical depth to generate insights, measure impact, and guide strategy across iCloud and related services.

Description
We are seeking an experienced Senior Data Scientist to drive high-impact analytics for iCloud and Apple's cloud services. You will collaborate across engineering, product, and business teams to uncover opportunities, optimize user experiences, and influence decisions at global scale.

This role calls for a balance of technical depth, strategic thinking, and strong communication. You'll deliver insights that shape the trajectory of Apple Services, lead complex analytical projects, and expand a culture of data-informed decision-making across ASE.","responsibilities":"Partner with Product, Engineering, Design, and Business leads to define priorities, shape measurement frameworks, and translate insights into growth strategies for iCloud.
Deliver data-informed narratives for senior leadership that directly inform product and business roadmaps.
Drive organizational alignment by clarifying analytical outcomes and implications for diverse stakeholders.
Design performance metrics that capture product health, user engagement, and business impact across iCloud services.
Lead controlled experiments, apply causal frameworks and marketing mix models to evaluate initiatives at scale.
Conduct deep behavioral analysis to uncover engagement, retention, and churn drivers, then translate findings into actionable strategies.
Collaborate with Data Engineering to define instrumentation and build reliable data pipelines across ASE.
Develop and productionize ML and statistical models that enable predictive and diagnostic insights for product decisions.
Leverage generative AI to automate analytical workflows and expand measurement capabilities at scale.

Preferred Qualifications
Advanced degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Economics, or related discipline).
Practical experience with experimentation (A/B testing, Multi-armed bandit) and advanced causal inference methods (e.g., matched markets, synthetic controls).
Hands-on experience with large-scale data ecosystems (Hive, Spark, Presto, etc.) and building scalable, reproducible analytical pipelines.
Demonstrated success influencing or leading projects within matrixed, cross-functional environments.

Minimum Qualifications
7+ years of experience in data analytics or data science, with 5+ years in data science applying statistical modeling, or Machine Learning in production.
Proven ability to influence cross-functional initiatives and shape product or business strategy using data-informed insights.
Strong communicator skilled at framing complex technical concepts for senior and executive audiences.
Deep expertise in product analytics, launch reporting, and impact measurement.
Proficiency in SQL and at least one programming language (Python, R, etc.), with strong statistical and experimental design foundations.
Ability to structure ambiguous problem spaces, prioritize effectively, and deliver actionable insights.
Exposure to applying generative AI or ML-based agents (ex. Claude) to automate analytical workflows and build data products.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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