Senior Data Scientist - Machine Learning

Accountabilities:

• Design, train, validate, and refine supervised machine learning models that identify providers and billing patterns associated with healthcare fraud, waste, and abuse risk.
• Develop robust labeling and entity-resolution approaches using investigative case data, payer feedback, public enforcement records, and exclusion data.
• Design validation strategies for complex real-world conditions, including delayed labels, incomplete historical coverage, extreme class imbalance, data leakage risks, and rapidly evolving fraud schemes.
• Engineer features directly against very large claims datasets using Python and SQL, collaborating with data engineering and business intelligence teams to ensure scalable processing.
• Produce actionable model outputs that provide investigators with ranked risk scores, human-readable rationales, relevant claims, and supporting evidence.
• Deploy machine learning models into production and establish reliable practices for scheduling, model versioning, monitoring, drift detection, and ongoing model health.
• Help define and establish the modeling and deployment standards that will guide future data science initiatives.
• Partner with fraud, waste, and abuse subject matter experts to distinguish genuine anomalies from patterns caused by coverage policies, claim edits, or other legitimate factors.
• Communicate methodologies, assumptions, limitations, and analytical findings effectively to both technical and non-technical audiences, including clients and program stakeholders.
• Contribute to a strong machine learning practice by establishing scalable approaches, documentation, and best practices for future development.

Requirements:

• Master’s degree in statistics, computer science, engineering, applied mathematics, or another quantitative discipline, or a bachelor’s degree combined with equivalent hands-on experience.
• At least 5 years of experience building, validating, and delivering supervised machine learning models using real-world data, particularly where labels may be incomplete, delayed, or biased.
• Demonstrated experience deploying machine learning models into production, including scheduling, version control, monitoring, and collaboration with data engineering teams.
• Strong proficiency in Python and SQL, including experience performing feature engineering directly within large-scale data warehouses rather than relying on local data extraction.
• At least 2 years of experience working with healthcare claims data, such as Medicare, Medicaid, or commercial claims, and familiarity with coding systems including ICD-10, CPT, HCPCS, and DRG.
• Strong understanding of validation techniques for imbalanced and temporally shifting datasets, including out-of-time evaluation, leakage detection, calibration, and precision-focused ranking metrics.
• Ability to translate complex model outputs into clear, understandable insights for investigators and other non-technical users.
• Strong communication skills and the ability to explain and defend analytical methodologies to technical audiences while presenting findings effectively to clients and stakeholders.
• Experience with graph or network analytics, entity resolution, and record linkage is highly desirable.
• Knowledge of positive-unlabeled, semi-supervised, or active learning approaches is a plus, particularly for capacity-constrained investigative workflows.
• Experience developing models in regulated or adverse-action environments where explainability, fairness, and responsible model use are important is beneficial.
• Familiarity with anomaly detection, peer-group construction, case-mix methodologies such as HCC, AWS, Snowflake, Snowpark, or model lifecycle tooling is preferred.
• Experience with healthcare fraud, waste and abuse analytics, program integrity, multi-payer datasets, coverage policies, or claims edits such as NCCI is an advantage.
• Must be able to obtain and maintain a Public Trust.
• Candidates must reside in the United States; work visa sponsorship is not available for this position.

Benefits:

• Salary: Expected range of $123,250–$166,750 per year, with actual compensation determined by experience, geographic location, and applicable contractual requirements.
• Remote work: Fully remote position with a standard 40-hour workweek.
• Healthcare: Multiple medical plan options, including plans with Health Savings Accounts, as well as dental and vision coverage.
• Retirement: 401(k) plan with company matching and pre- and post-tax contribution options, subject to applicable IRS limits.
• Paid time off: Vacation, sick, and personal leave, plus paid holidays. New employees typically receive 15 days of paid leave and 10 paid holidays annually, prorated according to hire date.
• Family and personal leave: Paid parental, military, bereavement, and jury-duty leave, along with up to 160 hours of paid family leave during a rolling 12-month period for eligible employees.
• Financial protection: Short- and long-term disability, life insurance, accidental death and dismemberment, personal accident, critical illness, and business travel and accident insurance.
• Flexibility: Full-flex work arrangements designed to support work-life balance.
• Travel: Less than 10% travel expected.
• Career development: Opportunity to establish a machine learning practice, define production standards, and grow alongside professionals working on complex data science and engineering challenges.