Note: The job is a remote job and is open to candidates in USA. BigBear.ai provides AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. The Data Scientist designs and builds transparent, rule-weighted risk-scoring logic, develops interpretable model architectures, and partners with engineering and application teams to productionize, validate, monitor, and document scoring solutions.
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
Skills
- All applicants must currently reside in the United States
- This position is fully remote, however travel in the DMV area may be expected
- **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)
- 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
- AWS Certification – Machine Learning
- Temporal Knowledge Graphs (TKG)
- SQL and SPARQL experience
- Familiarity with adjudicative, compliance, fraud/risk, or other risk-scoring domains (preferred/bonus)
Benefits
- This position is fully remote
Company Overview