Data Scientist, Lead

Bigbear.ai; McLean (VA); Hybrid; USD 150,000 - 210,000; Full time

Job summary

BigBear.ai in the United States is seeking a Data Scientist to design and build the v1 rule-weighted scoring logic that converts normalized risk signals into a transparent, defensible score, with explainability preserved for adjudicator workflows and audit needs.

This fully remote role requires Top Secret clearance and travel within the DMV area. You will collaborate with data engineering and application teams to productionize scoring, validate models, and document methodology.

Qualifications

• Bachelor's or higher in a technical field and several years of applied data science experience.
• Experience delivering scoring, ranking, or decision-support models.
• Experience implementing interpretable approaches (rule-based or SHAP explanations).
• Strong Python skills with scikit-learn and data science workflows.

Responsibilities

• Design v1 rule-weighted scoring logic from normalized risk signals.
• Define scoring framework components: features, weights, thresholds, guardrails.
• Create interpretable explanations for scores and drivers for adjudicator reviews.
• Design architecture to evolve from rules to interpretable ML models with auditability.
• Collaborate with data engineering and application teams to productionize scoring.

Skills

Strong Python skills

Explainability/interpretability

Stakeholder communication

Education

Bachelor's Degree

Master's Degree

PhD

Tools

Python

scikit-learn

SHAP

SQL

AWS SageMaker

Lambda

Glue

Neptune

Jupyter

Job description

Residency

All applicants must currently reside in the United States.

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.

This position is fully remote, however travel in the DMV area will 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, 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).
• IC/DoD experience

Tools & Technical Environment

• Python (scikit-learn, SHAP)
• Neptune
• Jupyter
• SQL
• AWS SageMaker
• Lambda
• Glue

What we'd like you to have

• 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.

Pay transparency

Please 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 thalue of any benefits offered.

About BigBear.ai

BigBear.ai is a leading provider of AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai’s predictive analytics capabilities in highly complex, distributed, mission-based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit https://bigbear.ai/ and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai.

BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.