Risk Modeler – Analytic Modeling & Risk Scoring

3gimbals; Quantico (VA); On-site; USD 130,000 - 170,000; Full time

Job summary

3gimbals in Quantico seeks a Risk Modeler to design, validate, and operationalize analytic models quantifying risk for mission analysts and decision-makers. You will build statistical, ML, and scoring models from multi-source data and graph relationships, emphasizing explainability and uncertainty.

Proficiency in Python and ML frameworks is essential, with a focus on production-ready implementations and clear documentation for stakeholders.

Qualifications

• 4+ years developing quantitative, statistical, or ML models.
• Strong Python and scientific stack: NumPy, pandas, scikit-learn.
• Solid foundation in statistics, probability, and quantitative methods.
• Experience with ML frameworks and model validation/back-testing.
• Ability to work with large, multi-source datasets.

Responsibilities

• Design and develop statistical, probabilistic, and ML risk models.
• Validate models for accuracy, robustness, bias, and stability.
• Operationalize models into production pipelines.
• Document model assumptions, methodology, and limitations.
• Support ATO processes and model governance.

Skills

Quantitative modeling

Python programming

Statistics

Machine learning

Explainability

Data analysis

Stakeholder communication

Education

Advanced degree in a quantitative field

Tools

NumPy

pandas

scikit-learn

XGBoost

PyTorch

TensorFlow

SHAP

LIME

SQL

Docker

Kubernetes

Job description

We are seeking a Risk Modeler to develop, validate, and operationalize the analytic models that quantify and prioritize risk within our unclassified PAI/CAI-based analytic platform. This role is responsible for designing statistical, machine-learning, and scoring models that turn multi-source data and graph relationships into defensible, explainable risk indicators for mission analysts and decision-makers. The ideal candidate pairs rigorous quantitative methods with a practical focus on explainability, uncertainty, and operational use.

Key Responsibilities

Model Development

• Design and develop statistical, probabilistic, and machine-learning models to quantify risk across entities, events, and networks
• Build risk-scoring, ranking, and prioritization methodologies from multi-source data
• Develop anomaly-detection, forecasting, and pattern-analysis models
• Incorporate geospatial and temporal features into risk models where applicable

Validation & Explainability

• Validate models for accuracy, robustness, bias, and stability, including back-testing and sensitivity analysis
• Ensure models are explainable, defensible, and appropriately caveated for analytic use
• Quantify and communicate uncertainty, confidence, and the impact of data quality
• Document model assumptions, methodology, and limitations

Operationalization

• Work with data and platform engineers to deploy models into production pipelines
• Monitor model performance and drift; maintain retraining and evaluation workflows
• Translate analyst and mission requirements into clear model specifications

Security & Compliance

• Ensure models and data handling meet security requirements for sensitive environments
• Support Authority to Operate (ATO) processes, model governance, and compliance frameworks

Required Qualifications

Technical Expertise

• 4+ years developing quantitative, statistical, or machine-learning models
• Strong programming skills in Python and its scientific stack (NumPy, pandas, scikit-learn); R a plus
• Solid foundation in statistics, probability, and quantitative methods
• Experience with ML frameworks (scikit-learn, XGBoost, PyTorch, or TensorFlow)
• Experience with model validation, evaluation metrics, and back-testing
• Ability to work with large, messy, multi-source datasets

Modeling Breadth

• Experience with risk scoring, anomaly detection, forecasting, or predictive modeling
• Familiarity with explainability techniques (SHAP, LIME) and model documentation
• Understanding of uncertainty quantification

Domain Knowledge

• Experience translating operational or analytic requirements into models
• Ability to communicate methodology and results clearly to non-technical stakeholders

Preferred Qualifications

• Active security clearance or ability to obtain one
• Experience in government, defense, or intelligence contracting environments
• Domain experience in one or more of: supply chain risk, maritime or geospatial risk, or threat/security analytics
• Experience with geospatial-temporal modeling (GIS, spatial statistics)
• Familiarity with graph-based features or network analytics as model inputs
• Familiarity with PAI/CAI data sources
• Advanced degree in a quantitative field (statistics, operations research, data science, applied mathematics, or economics)

Technical Environment

• Languages: Python (R, SQL a plus)
• ML / Stats: scikit-learn, XGBoost, PyTorch/TensorFlow, statsmodels
• Explainability: SHAP, LIME, model documentation practices
• Geospatial: GIS and spatial-statistics tooling (where applicable)
• Infrastructure: Docker, Kubernetes, cloud platforms (AWS GovCloud, Azure Government), MLOps tooling
• Security: Secure data handling, model governance

This role turns the platform's data and relationships into decision-ready signals: rigorous, transparent risk models that analysts and decision-makers can trust and defend.