Senior Data Scientist – Machine Learning

Role Overview

As the Senior Data Scientist for Machine Learning supporting the Healthcare Fraud Prevention Partnership (HFPP), you will develop predictive models against a multi-billion record claims warehouse assembled from dozens of public and private healthcare payers. This is a senior individual contributor position without direct reports, and it is the only role on the team focused primarily on machine learning, meaning the Senior Data Scientist will be establishing practice rather than joining one.

What You Will Do

Designing, training and validating supervised models that score providers and billing patterns for FWA risk, using investigative case-level data, payer feedback on referred leads, and public exclusion and enforcement data as labels. Delivering output that supports action, including human-readable rationale and claim-level evidence alongside the score.

Why It Might Be a Fit

5+ years of building, validating and delivering supervised machine learning models on real-world data, including work in which labels were incomplete, delayed or biased. Experience deploying models into production and maintaining them: scheduling execution, versioning and drift monitoring, with data engineers.

Requirements

• Master’s in a quantitative field (statistics, computer science, engineering, applied mathematics or related), or a Bachelor’s with equivalent hands-on experience.
• 5+ years building, validating and delivering supervised machine learning models on real-world data, including work in which labels were incomplete, delayed or biased.
• Experience deploying models into production and maintaining them: scheduling execution, versioning and drift monitoring, with data engineers.
• Python and SQL, including feature engineering within the data warehouse at very large scale rather than extracting to a local environment.
• 2+ years working with healthcare claims data (Medicare, Medicaid or commercial) and coding systems (e.g., ICD-10, CPT, HCPCS, DRG).
• Experience with validation design for imbalanced, temporally shifting problems: out-of-time evaluation, leakage detection, calibration and precision-focused metrics over ranked output.

Benefits

• Medical plan options
• Dental plan options
• Vision plan
• 401(k) plan with company match
• Full flex work weeks
• Paid time off plans (vacation, sick, personal time, holidays, paid parental, military, bereavement and jury duty leave)
• Paid leave (15 days per calendar year)
• Paid holidays (10 per year)
• Paid Family Leave program (up to 160 hours of paid leave in a rolling 12 month period)
• Short and long-term disability benefits
• Life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance