Midi Health
Senior Data Analyst - Clinician Experience
$175K
Hybrid - Palo Alto or San Francisco +1 · Remote · Senior · Full-time · Posted Aug 27, 2026
- Location
- Hybrid - Palo Alto or San Francisco
Remote - Experience
- 5+ years of data analytics experience; healthcare experience is a strong preference.
- Education
- Bachelor’s or Master’s degree in a quantitative discipline or equivalent practical experience.
- Visa sponsorship
- Unable to provide visa sponsorship; candidates must be authorized to work in the U.S. without current or future sponsorship needs.
About the job
Own the analytics framework for the clinician workforce, connecting lifecycle, operational performance, clinical quality, and provider behaviors to patient and business outcomes.
What you'll do
- Analyze clinician recruiting, onboarding, credentialing, licensing, retention, and attrition to improve workforce capacity.
- Build provider performance scorecards and transcript-based scoring frameworks covering operational SLAs, clinical quality, safety, and empathy.
- Analyze how clinician behaviors and performance relate to patient retention, NPS, and treatment adherence; provide executive dashboards and guidance.
What they ask for
- Expert-level SQL and advanced Python for analytics, modeling, and text or sentiment processing.
- Experience designing executive-ready dashboards in BI tools.
- Familiarity with text mining, sentiment analysis, or prompt-based LLM evaluation frameworks.
- Understanding of clinical operations, EMR/EHR workflows, and clinical quality metrics.
Nice to have
- Healthcare experience, particularly in Digital Health, Telehealth, tech-enabled medical groups, or Health Operations; People Analytics/People Data Science with operational telemetry is also welcomed.
- Experience with cohort analysis, retention modeling, and behavioral or qualitative scoring models.
Pay
150-175K USD base salary
Tools
SQL, Python, Looker, Tableau, PowerBI, Snowflake, BigQuery, Databricks
AI focus
Use sentiment and text analytics LLM tools to score clinician interactions and extract insights from transcripts; adopt AI tools, including LLMs for coding efficiency and automated insights, to accelerate analytics workflows.
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