Senior Data Scientist - Machine Learning

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist - Machine Learning based in United States.

This is a senior individual contributor opportunity to establish and lead the machine learning practice within a mature healthcare fraud, waste, and abuse analytics program. You’ll work with a multi-billion-record claims warehouse combining data from numerous public and private healthcare payers. The role offers unusual ownership, with responsibility for designing models as well as defining how they are validated, deployed, monitored, and maintained in production. You’ll solve challenging machine learning problems involving delayed and incomplete labels, severe class imbalance, temporal shifts, and evolving fraud patterns. Your work will directly support investigators by turning model predictions into ranked, explainable findings backed by claim-level evidence. You’ll collaborate closely with healthcare fraud experts, data engineers, BI professionals, and external stakeholders to ensure models are accurate, practical, and actionable. This role is ideal for a hands-on machine learning expert who wants to shape both technical practice and real-world outcomes at significant scale.

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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1