Analytics Data Scientist - Quality Engineering, Siri

Apple Inc

Quick summary

Work type: On-site
Location: San Francisco, CA
Salary: $172,100–$258,600 / yr
Posted: 43 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $156k

This role $215k

$80k most similar roles pay here $278k

This role pays more than 85% of similar roles. Most pay $126,800–$185,415 — the shaded band above. At the midpoint, this role pays about $215k versus about $156k 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 969 open roles on FindRole.

Listed pay typically runs $163,300–$272,100 across 756 roles with salary data.

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At a glance

TL;DR · Analytics Data Scientist - Quality Engineering, Siri

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The Siri Quality Engineering team at Apple seeks an experienced Analytics Data Scientist to join their mission-driven group. This role involves building and maintaining a reliable data lakehouse with real-time pipelines, ensuring teams can trust the data they use for decision-making. Day-to-day responsibilities include defining KPIs, automating executive reports, conducting deep dives into causal analysis, recommending actions based on predictive models, and translating insights into actionable narratives through dashboards. The ideal candidate will have 5+ years of experience in product or operations analytics, expertise in SQL and Python (pandas, NumPy), proficiency in statistics and experiment design, and the ability to communicate effectively with both technical and executive stakeholders. Additional skills such as forecasting, optimization techniques, MLOps basics, and agentic AI-assisted coding are highly valued.

Skills

Python pandas NumPy scikit-learn SQL PySpark Tableau AWS CI/CD MLOps Kubernetes Docker Prometheus Grafana Git Jupyter Notebook PostgreSQL Snowflake BigQuery

What you'll do

Lead analytics across four modes to improve engineering efficiency and product quality.
Define KPIs and metrics; automate executive-ready reporting and anomaly detection.
Explain drivers through deep dives, causal analysis, and cohorting studies.
Recommend actions using optimization techniques, simulations, and experiment results.
Build and productionize models for forecasting, classification, and risk scoring.

What we're looking for

5+ years of product/ops analytics or data science experience
Expertise in SQL and proficiency in Python (pandas, NumPy, scikit-learn, PySpark)
Strong background in statistics and experiment design including hypothesis testing and causal inference
Track record of translating analysis into actionable decisions with measurable impact
Ability to communicate effectively with both technical and executive stakeholders
Experience with agentic AI-assisted coding tools

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