Job Description
Job Description
Overview
The Data Scientist designs and builds the v1 rule-weighted composite scoring logic that turns normalized risk signals into a transparent, defensible score. This role also prepares the scoring approach and model architecture for future interpretable ML-based scoring—ensuring explainability is preserved for adjudicator-facing workflows and audit needs. The ideal candidate blends practical applied data science with strong judgment around interpretability, traceability, and operational usability.
Responsibilities
- Build and tune v1 rule-weighted composite scoring logic using normalized inputs from the common risk-signal schema.
- Define scoring framework components (feature groupings, weights, thresholds, guardrails, and handling of missing/partial data).
- Create interpretable explanations for scores and drivers suitable for adjudicator review (reason codes, key contributing signals, and traceable logic).
- Design the scoring architecture to support evolution from rules/weights to interpretable ML models while maintaining auditability.
- Prototype and evaluate interpretable model classes and explanation methods (e.g., SHAP-based explanations, constrained/monotonic models where appropriate, and rule-based hybrids).
- Partner with data engineering and application teams to productionize scoring logic (data inputs, contracts, output formats, and performance expectations).
- Establish validation and monitoring approaches (basic model/score QA, drift indicators, and score distribution checks).
- Document scoring methodology, assumptions, and limitations for stakeholder understanding and accreditation/compliance artifacts as needed.
Qualifications
- Clearance: Must maintain an active TS/SCI security clearance
- Bachelor's Degree and 8 to 10 years of experience; Master's Degree and 6 to 8 years of experience; PhD and 3 to 5 years of experience (in lieu of Bachelor’s degree, 6 additional years of relevant experience)
- 3–5 years of applied data science experience delivering scoring, ranking, or decision-support models.
- Experience implementing interpretable approaches (rule-based systems, transparent composite scores, and/or explainability methods such as SHAP).
- Strong Python skills, including scikit-learn and common data science workflows.
- Hands-on experience with SQL for data analysis, feature development, and validation.
- Ability to communicate scoring logic clearly to technical and non-technical stakeholders (including explaining tradeoffs between accuracy and interpretability).
- Familiarity with adjudicative, compliance, fraud/risk, or other risk-scoring domains (preferred/bonus).