Senior Analytics Engineer, Health Operations (MSO)

Build the analytical backbone for how health operations perform and where they’re headed. In this hybrid role in Cupertino, CA, you’ll lead end-to-end measurement, scalable data modeling, and diagnostic analytics that help translate complex healthcare operations data into executive-ready insights for cross-functional teams.

Compensation: USD 175,500 - 263,800 per year. Experience: 7+ years. Education: Bachelor’s degree in Statistics, Biostatistics, Business, Healthcare, Operations Research, Computer Science, or a related field.

What you’ll do

• Own the analytical frameworks, measurement architecture, and diagnostic models that clarify health operations performance and future direction.
• Design robust data models, transform complex health operations datasets, and establish scalable data structures for reliable reporting and advanced analytics.
• Build and take responsibility for end-to-end measurement plans, from metric definitions to deployment and evaluation, for new health services and features.
• Partner with cross-functional teams across clinical, operational, product, legal, and engineering to define success measurement, including adoption, operational efficiency, and customer experience, and turn that data into decisions.
• Act as a technical and strategic leader by driving analytics engineering best practices and helping shape a world-class healthcare analytics function at scale.

What you bring

• Bachelor’s degree in Statistics, Biostatistics, Business, Healthcare, Operations Research, Computer Science, or a related field.
• 7+ years of experience in business insights or strategic analytics, with 2+ years in a lead or senior individual contributor capacity.
• Proven ability to apply quantitative methods, statistical modeling, and business and financial analysis to evaluate product performance, operational workflows, and executive priorities.
• Ability to design measurement frameworks from zero to one, including metric definitions, calculation logic, and validation methods.
• Ability to influence clinical and operational stakeholders and drive cross-functional alignment without direct authority.
• Capability to translate ambiguous business problems into structured analytical frameworks and actionable recommendations.
• Advanced SQL proficiency and experience with Python (or equivalent) for data wrangling, exploratory analysis, and automated production-ready analytical workflows.
• Proven experience building intuitive data visualizations, executive dashboards, and reports using Tableau or AWS QuickSight.
• Exceptional executive presence and storytelling skills, translating technical findings for clinical, operational, and non-technical stakeholders.
• Highly organized, self-directed, able to prioritize and deliver high-impact work under tight timelines in ambiguous environments.
• Ability to be onsite; this role is hybrid.

Tools you’ll use

• SQL
• Python
• Tableau
• AWS QuickSight

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount through voluntary participation in the Apple Employee Stock Purchase Plan
• Additionally, this role might be eligible for discretionary bonuses or commission payments, as well as relocation

Preferred qualifications

• Deep business acumen to understand health operational workflows, clinical program performance, and patient experience drivers, and apply that context to build strategically meaningful analyses.
• Familiarity with formal performance management and goal-setting frameworks (for example, OKRs, operational scorecards, quality benchmarks) and how data infrastructure supports accountability at scale.
• Background in health operations, clinical operations, or healthcare delivery analytics, with understanding of how patient experience, clinical quality, care utilization, and operational efficiency metrics connect to care outcomes.
• Hands-on experience with modern data orchestration tools and interest in applying machine learning, LLMs, or AI-assisted workflows to accelerate clinical and operational analytics.