Senior Data Scientist - Machine Learning

As the Senior Data Scientist for Machine Learning supporting the Healthcare Fraud Prevention Partnership (HFPP), you will be the first dedicated machine learning practitioner at the Trusted Third Party (TTP), an established Fraud, Waste and Abuse (FWA) analytics program. You will develop predictive models against a multi-billion record claims warehouse assembled from dozens of public and private healthcare payers, and you will establish how machine learning models move from development into production on this program.

The data, the subject matter experts and the payer partnerships are already in place; the modeling capability is yours to build. 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.

***Work visa sponsorship will not be provided for this position. This is a remote role. Candidates must reside in the United States.

MEANINGFUL WORK AND PERSONAL IMPACT:

• 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, including the entity resolution to link enforcement records to providers in claims.

• Designing validation for the actual conditions: labels lagging billing behavior by years, coverage limited to leads previously referred, extreme class imbalance, and schemes that shift faster than confirmation arrives.

• Engineering features against billions of claim records within the warehouse rather than extracting data to local memory, using Python and SQL, alongside data engineers and Business Intelligence Developers.

• Delivering output that supports action. Investigators need the specific claims, the pattern and the basis for the finding, so each model carries a human-readable rationale and claim-level evidence alongside the score, adjusted for case mix and specialty and ranked so that precision at the top of the review queue is the operative measure.

• Deploying models into production and keeping them healthy, including scheduled execution, versioning and drift monitoring, and establishing the modeling and deployment practices the Data Science team adopts going forward.

• Collaborating with FWA Subject Matter Experts to separate genuine anomalies from patterns explained by coverage policy or claim edits, and communicating methodology and limitations to HFPP Partners and stakeholders so that output is adopted and acted upon.

WHAT YOU'LL NEED TO SUCCEED:

• 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.

• Ability to explain model output to a non-technical investigator, defend methodology to technical audiences, and present analytic outcomes to clients and stakeholders.

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DESIRED QUALIFICATIONS:

• Graph or network analytics, entity resolution and record linkage for identifying collusive relationships across payers.

• Positive-unlabeled, semi-supervised or active learning against a capacity-constrained review queue.

• Modeling in a regulated or adverse-action setting where explainability and fairness were requirements.

• Anomaly detection, peer-group construction and case-mix methods (e.g., HCC); AWS and/or Snowflake, including Snowpark or model lifecycle tooling.

• Healthcare FWA or program integrity datamining in multi-payer databases; payer coverage policy (LCDs, NCDs) and claim edits (e.g., NCCI).

SECURITY CLEARANCE LEVEL:

• Must be able to obtain/maintain Public Trust.

GDIT IS YOUR PLACE:

The GDIT HFPP TTP is the only data warehouse of its type anywhere, bringing many billions of claims from dozens of public and private payers together solely for fraud, waste and abuse analytics. The cross-payer visibility it provides exists nowhere else.

A combination uncommon in machine learning roles: mature data and established subject matter expertise in place, with the modeling and production practices yours to define.

OWN YOUR OPPORTUNITY:

Explore a career in data science and engineering at GDIT and you'll find endless opportunities to grow alongside colleagues who share your determination for solving complex data challenges.