ResidencyAll applicants must currently reside in the United StatesOverviewThe
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.
This position is fully remote, however travel in the DMV area may be expected.
What you will do- 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.
What you need to have- Clearance: Must maintain an active Top Secret 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, decision trees, matrix factorization, transparent composite scores, and/or explainability methods such as SHAP and permutation importance).
- 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).
What we'd like you to haveKey Behavioral Competencies - Explainability-first mindset: prioritizes transparency, traceability, and defensibility.
- Analytical rigor: validates assumptions, tests edge cases, and avoids "black-box" shortcuts.
- Collaboration: works effectively with data engineers and application teams to ensure scoring is usable and production-ready.
- Documentation discipline: produces clear, auditable artifacts that describe logic, drivers, and limitations.
Tools & Technical Skills- AWS Certification - Machine Learning
- Temporal Knowledge Graphs (TKG)
- SQL and SPARQL experience
Pay transparencyPlease note the targeted compensation range is provided as an estimate, and any actual compensation offer may vary depending on the needs of the company, or an applicant's skillset, competencies, experience, education, certifications, location, or other factors. The estimated range does not include the value of any benefits offered.
