Senior Analytics Engineer, Health Operations (MSO)

Description

Minimum Qualifications

• 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 at least 2 years in a lead or senior individual contributor capacity.
• Proven track record of applying quantitative methods, statistical modeling, and business /financial analysis to evaluate product performance, operational workflows, and executive priorities.
• Proven ability to design end-to-end measurement frameworks, including metric definitions, calculation logic, and validation methods, from zero to one for new initiatives.
• Demonstrated ability to influence clinical and operational stakeholders and drive cross-functional alignment without direct authority.
• Proven ability to translate complex, ambiguous business problems into structured analytical frameworks and actionable recommendations.
• Advanced SQL proficiency and experience with Python or equivalent for data wrangling, exploratory data analysis, and building automated, production-ready analytical workflows.
• Demonstrated track record of building intuitive data visualizations, executive dashboards, and reports using tools such as Tableau or AWS QuickSight.
• Exceptional executive presence and storytelling abilities, with demonstrated success translating technical findings into clear narratives for clinical, operational, and non-technical stakeholders.
• Highly organized, self-directed, and proven ability to navigate ambiguity, prioritize competing demands, and deliver high-impact results under tight timelines.
• Ability to be onsite; this role is a hybrid, in-person position

Preferred Qualifications

• Deep business acumen with the ability to rapidly 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 (e.g., OKRs, operational scorecards, quality benchmarks) and how data infrastructure supports accountability at scale.
• Background in health operations, clinical operations, or healthcare delivery analytics with an understanding of how patient experience, clinical quality, care utilization, and operational efficiency metrics work together to shape care outcomes.
• Hands-on experience with modern data orchestration tools and an active interest in applying machine learning, LLMs, or AI assisted workflows to accelerate clinical and operational analytics.