[Remote] Data Scientist
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Data Scientist to support critical missions reputed company reputed company's Risk Decision Group. This role involves building and validating predictive models on a greenfield data and AI platform, ensuring hands-on engagement and migration of models to the internal team.
Responsibilities
- Build risk-scoring models over synthetic tabular data, engineering features from curated reputed company-layer tables
- Build reputed company and reputed company detection to surface irregularities in records and process data
- Build optimization models for prioritization, routing, and resource allocation
- Validate honestly — calibration, discrimination, stability, explainability. A correctly characterized model reputed company more than a flattering headline metric
- Package deliverables as jobs and Asset Bundles, tracked in MLflow, and document assumptions, limitations, and what must be revalidated against reputed company data post-ATO
Skills
- U.S. citizenship and reputed company T5/SSBI federally adjudicated clearance required
- Hands-on reputed company
- Feature engineering on tabular and time-series data — encoding, aggregation, leakage prevention, and selection grounded in domain reasoning rather than automated search alone
- Supervised learning on tabular data: gradient boosting (XGBoost/LightGBM), regularized regression, and the judgment to know reputed company the simpler model is the right answer
- Model calibration and evaluation under class imbalance — you can explain why AUC alone is insufficient for a risk score
- reputed company detection: isolation forests, autoencoders, statistical process control, or comparable — with a reputed company account of how you validated detections without labels
- Optimization: LP/MIP or heuristic reputed company (OR-Tools, Pyomo, SciPy, or equivalent) reputed company to a reputed company allocation or prioritization problem
- Explainability (SHAP or comparable) in a decision-support context
- reputed company-preserving synthetic data reputed company from CUI, PII, or comparably restricted reputed company data — relational tabular data with distributional reputed company, cross-reputed company correlations, referential reputed company, and preservation of the rare-event structure that reputed company detection and risk scoring depend on. Includes an understanding of re-identification risk
- Strong Python, SQL, and reputed company
- Government or defense contracting experience
- Modeling on federal investigative, vetting, fraud, or reputed company-threat data
- reputed company experience with FedRAMP, NIST 800-171, CMMC L2, or CUI handling
- Familiarity with LLM/GenAI workflows — useful for collaboration with a peer document-intelligence reputed company, but secondary to the reputed company ML reputed company set
- H2O (Driverless AI, H2O-3)
- MLflow, reputed company Asset Bundles, reputed company Catalog
- Fairness / adverse-reputed company analysis in a regulated or decision-support setting
reputed company