Statistical Risk Model Developer (Finance/Mortgage)

Statistical Risk Model Developer | Tysons Corner, VA (Hybrid) 🕒 Contract: 6 to 7 Months with Possible Extension We are seeking a highly analytical Statistical Risk Model Developer to support enterprise risk and advanced analytics initiatives within a large-scale, data-driven environment. This opportunity is ideal for professionals with deep expertise in statistical modeling, quantitative analysis, probabilistic methods, and predictive risk modeling. This is not a pure AI/ML or NLP engineering role. The team is specifically seeking candidates with a strong foundation in quantitative risk modeling and advanced statistical methodologies who can apply those skills to complex enterprise risk scenarios. Cybersecurity domain knowledge is beneficial but can be learned on the job. Key Responsibilities: ▪ Develop, validate, and enhance sophisticated statistical and quantitative risk models ▪ Apply probabilistic theory, predictive analytics, and mathematical modeling techniques to large and complex datasets ▪ Identify trends, anomalies, and risk indicators to support strategic business decisions ▪ Design impactful data visualizations and present analytical findings to technical and executive stakeholders ▪ Collaborate with cross-functional teams to strengthen enterprise risk management capabilities Required Qualifications: ✔ 10+ years of experience in statistical modeling, quantitative risk modeling, or advanced analytics ✔ PhD or advanced degree in Mathematics, Statistics, Physics, Econometrics, or related quantitative field preferred ✔ Strong expertise in: • Statistical & Predictive Modeling • Quantitative Risk Analysis • Probabilistic Theory • Dynamic Systems Theory • Data Mining & Data Modeling ✔ Excellent communication and presentation skills with the ability to translate complex analytical concepts into business insights Preferred Technical Skills: ✔ Python, SQL, Jupyter Notebook/JupyterLab, VS Code ✔ Experience with Power BI, Plotly, or other visualization platforms ✔ Familiarity with machine learning concepts is a plus ✔ Exposure to cybersecurity frameworks or risk analytics environments preferred Ideal Backgrounds: We are particularly interested in candidates with experience in: • Quantitative Risk Modeling • Statistical Research & Analytics • Financial or Enterprise Risk Analytics • Econometrics • Applied Mathematics & Advanced Statistics • Predictive Risk Modeling If you have a strong statistical and quantitative modeling background and are interested in solving complex enterprise risk challenge, feel free to put in your application.