What you’ll do in this role:

Build and maintain the forecast model for network capacity vs. patient demand across all 50 state markets and multiple clinical care programs, and use it to guide where and when we invest in supply
Partner with Network Ops and Clinical Talent to deliver measurement and insights that support clinician recruitment and onboarding and strengthen retention
Develop frameworks to measure the impact of clinical quality, therapeutic alliance, and patient panel balance at both the clinician and market level
Partner with Product and Engineering to design and run experiments on patient scheduling experiences and clinician-facing product features
Serve as the primary analytics partner for the Network POD, explaining the drivers behind changes in performance metrics, tying them to OKRs, clinical, and financial outcomes, and presenting insights and experiment results to leadership
Join a Data team building AI-powered analytics infrastructure that governs metric reliability and unlocks deeper analysis, including developing and documenting the data models behind KPI reporting and agentic analytics
Solve ambiguous problems creatively with systematic, scalable solutions

Requirements:

8+ years of analytics experience, with a Master's degree or equivalent practical experience
Expert-level SQL and data modeling in a cloud data warehouse (ex: BigQuery/GCP), with a track record of writing clean, performant, well-documented code
Hands-on experience developing and scaling a dbt project, including testing, documentation, and governed metric definitions
Strong forecasting and econometric modeling skills, ideally paired with operations research methods (capacity planning, supply-demand optimization, resource allocation)
Experience designing, running, and interpreting A/B tests and other experiments, including knowing when a clean experiment isn't possible and what to do instead
Proficiency with data visualization tools (ex: Tableau) to build dashboards and tell clear stories with data
Fluency with AI tools and MCPs, and a habit of using them to accelerate everything from data exploration to insight generation
Ability to turn complex analyses into clear business recommendations, with excellent written and verbal communication for audiences ranging from engineers to executives
A self-starter mindset: you're comfortable with ambiguity, set your own priorities, and thrive in a fast-moving startup environment

What you’ll do in this role:

Build and maintain the forecast model for network capacity vs. patient demand across all 50 state markets and multiple clinical care programs, and use it to guide where and when we invest in supply
Partner with Network Ops and Clinical Talent to deliver measurement and insights that support clinician recruitment and onboarding and strengthen retention
Develop frameworks to measure the impact of clinical quality, therapeutic alliance, and patient panel balance at both the clinician and market level
Partner with Product and Engineering to design and run experiments on patient scheduling experiences and clinician-facing product features
Serve as the primary analytics partner for the Network POD, explaining the drivers behind changes in performance metrics, tying them to OKRs, clinical, and financial outcomes, and presenting insights and experiment results to leadership
Join a Data team building AI-powered analytics infrastructure that governs metric reliability and unlocks deeper analysis, including developing and documenting the data models behind KPI reporting and agentic analytics
Solve ambiguous problems creatively with systematic, scalable solutions

Requirements:

8+ years of analytics experience, with a Master's degree or equivalent practical experience
Expert-level SQL and data modeling in a cloud data warehouse (ex: BigQuery/GCP), with a track record of writing clean, performant, well-documented code
Hands-on experience developing and scaling a dbt project, including testing, documentation, and governed metric definitions
Strong forecasting and econometric modeling skills, ideally paired with operations research methods (capacity planning, supply-demand optimization, resource allocation)
Experience designing, running, and interpreting A/B tests and other experiments, including knowing when a clean experiment isn't possible and what to do instead
Proficiency with data visualization tools (ex: Tableau) to build dashboards and tell clear stories with data
Fluency with AI tools and MCPs, and a habit of using them to accelerate everything from data exploration to insight generation
Ability to turn complex analyses into clear business recommendations, with excellent written and verbal communication for audiences ranging from engineers to executives
A self-starter mindset: you're comfortable with ambiguity, set your own priorities, and thrive in a fast-moving startup environment

Senior Data Analyst, Network Optimization

Skills