Staff Data Scientist, Clinical Performance

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$160K-$200K not verified - source not recorded; timestamp not recorded

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

61% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Staff Data Scientist, Clinical Performance San Francisco, NYC, Boston, or Remote, San Francisco, California, United States The Opportunity As a Staff Data Scientist on the Clinical Performance team, you will be the lead architect of the methodologies that prove Pearl Health's impact on the American healthcare system. You will tackle the massive challenge of untangling overlapping clinical interventions to isolate exactly what drives better patient outcomes and financial sustainability. Reporting to the Senior Director of Clinical Performance, you will play a defining role in building the feedback system that guides our company strategy and validates our mission to empower primary care providers. While your primary focus is evaluative, you will be a key player in the broader clinical DS ecosystem, collaborating on patient risk stratification and building the forecasting engines that predict our quality performance across various value-based care programs. What You'll Do You will lead the design and implementation of advanced causal inference and statistical frameworks to measure and forecast the effectiveness of Pearl's clinical products and operational services. - Architect Causal Frameworks: Design and build the scalable systems required to conduct rigorous impact analyses, moving beyond simple correlations to isolate the true "Pearl Effect" on patient populations. Set the technical bar for how we handle complex data challenges, including non-randomized treatment assignment, selection bias, and compounding intervention effects. - Forecast Quality & Performance: Develop predictive models to issue forecasts for clinical quality measures (including eCQMs in MSSP and claims-based measures in REACH and LEAD). This includes establishing the

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