Analytics Data Scientist - Quality Engineering, Siri

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.

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