Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.
The Siri Quality Engineering team is seeking an Analytics Data Scientist to build a Curated, Accurate, Reliable data lake-a governed lakehouse with point-in-time and near-real-time pipelines, and a clear semantic layer-so teams trust and use data. You will ultimately be part of a collaborative team that's responsible for understanding how well our products work, advocating for critical changes, and representing how they'll be experienced by our customers. We deliver descriptive, diagnostic, prescriptive, and predictive analytics that show what's happening, explain why, recommend what to do, and empower decision automation. We publish certified metrics and models used in executive-facing reports, raising product quality, speeding releases, and reducing risk across all Apple platforms and locales. As a member of our passionate group in Siri, you will have the unique and rewarding opportunity to shape and improve our products to delight and inspire millions of Apple's customers every day.
Turning data into decisions. Lead analytics across four modes-Descriptive, Diagnostic, Prescriptive, Predictive-to improve engineering efficiency and product quality.
Define KPIs and metrics; automate exec-ready reporting and anomaly detection.
Explain drivers with deep dives, causal/correlation analysis, and cohorting.
Recommend actions using optimization, simulations, and experiment results.
Build and productionize models (forecasting, classification, risk/quality scoring).
Translate insight to action with clear narratives, dashboards, and decision rules.
Standardize definitions (data dictionary, glossary surface and drive data-quality fixes with partners.
Run A/B tests; design guardrails; monitor impact.
5+ years in product/ops analytics or data science.
Expert SQL; strong Python (pandas, NumPy, scikit-learn or PySpark).
Statistics and experiment design (hypothesis testing, power, basic causal inference).
Proven record of turning analysis and models into shipped decisions with measurable impact.
Excellent communication with both technical and executive audiences.
Experience with agentic AI-assisted coding.
Forecasting/time-series; optimization (LP/MIP) or simulation.
MLOps basics (versioning, evaluation, monitoring).
Tableau or similar for self-service analytics.
Web Developer experience is a plus.
BS/MS in a quantitative field.
We’re a diverse collection of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. The people who work here have reinvented entire industries with the Mac, iPhone, iPad, and Apple Watch, as well as with services, including iTunes, the App Store, Apple Music, and Apple Pay. And the same passion for innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it.
There’s a place here for every kind of brilliant. Everyone here is an innovator, or an innovator-to-be, no matter what your team or your role. So bring your passion, courage, and original thinking and get ready to share it, because every new product, service, or feature we invent is the result of people working together to make each others’ ideas stronger. Innovation at this level depends on people who represent the variety of the human experience and inspire us with their own fresh perspectives. Together, we’ll do amazing work that can make a difference in people’s lives. Including your own. Learn more about working at Apple.