Data Scientist

Role Summary
This contract Data Scientist role focuses on developing and validating advanced predictive and prescriptive models within a high-trust federal environment. The position entails hands-on work with a greenfield data and AI platform, supporting critical missions through risk scoring, anomaly detection, and optimization models. The role offers an opportunity to contribute to mission-critical projects with a clear emphasis on model integrity, explainability, and data privacy.

Responsibilities

• Build and validate risk-scoring models using synthetic tabular data and engineered features from curated datasets.
• Develop anomaly and outlier detection techniques to identify irregularities in records and processing data streams.
• Design and implement optimization models for prioritization, routing, and resource allocation tasks.
• Ensure rigorous model validation, including calibration, discrimination, stability, and explainability assessments.
• Package and document model deliverables as jobs and Asset Bundles, tracked in MLflow, including assumptions, limitations, and revalidation needs.
• Support privacy-preserving synthetic data generation, maintaining distributional fidelity and re-identification risk management.
• Collaborate with multi-disciplinary teams to incorporate model feedback and improve model performance.
• Prepare comprehensive documentation, including assumptions and limitations, to facilitate knowledge transfer and audit readiness.
• Conduct fairness and adverse impact analysis within regulated decision-support environments.
• Maintain compliance with government security standards such as FedRAMP, NIST 800-171, and CUI handling requirements.

Qualifications

• This position requires eligibility for a U.S. Government security clearance. In accordance with federal law, U.S. citizenship is required (active T5/SSBI federally adjudicated security clearance).
• Hands-on experience with Databricks, Python, SQL, and Spark in a production environment.
• Proven expertise in feature engineering on tabular and time-series data, including encoding, aggregation, and leakage prevention.
• Strong background in supervised learning methods such as gradient boosting (XGBoost, LightGBM) and regularized regression.
• Knowledge of model calibration, evaluation under class imbalance, and explainability tools like SHAP.
• Experience with anomaly detection techniques such as isolation forests, autoencoders, or statistical process control, including validation without labels.
• Proficiency in optimization techniques using LP/MIP or heuristic methods like OR-Tools, Pyomo, or SciPy.
• Experience generating privacy-preserving synthetic data from CUI, PII, or similar source data, ensuring data utility and security.
• Government or defense contracting experience is preferred.
• Familiarity with FedRAMP, NIST 800-171, CMMC L2, or CUI handling is advantageous.
• Strong written and verbal communication skills, with the ability to document complex models and collaborate across technical and non-technical teams.
• Self-motivated with the ability to execute independently against fixed milestones with minimal oversight.

Publishing Pay Range: $75.00 - $80.00 hourly

This is a fully remote role and can be performed from any approved location within the United States.