Work Arrangement: Onsite – Pittsburgh, PA
Position Overview
We are seeking an experienced Senior Data Scientist / Credit Risk Model Developer for a leading banking client in Pittsburgh, PA. This is a specialized quantitative modeling position requiring strong experience developing and implementing credit risk models within a regulated banking environment. The ideal candidate will combine advanced statistical modeling expertise with a strong understanding of credit risk, model governance, validation, and regulatory requirements.
This is not a general Data Science or AI/ML role. Candidates should have direct experience developing models used for credit risk assessment, portfolio risk management, lending, or financial risk management.
Key Responsibilities
• Design, develop, enhance, and implement statistical and quantitative credit risk models.• Develop and support models involving Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and other credit-risk measures.• Perform model development activities including data analysis, segmentation, variable selection, calibration, testing, back-testing, and performance monitoring.• Support CECL, CCAR, stress testing, credit loss, allowance, and/or portfolio risk modeling initiatives as applicable.• Analyze large and complex banking, lending, customer, and credit datasets.• Apply statistical and machine-learning techniques appropriately within established banking risk-management frameworks.• Conduct model performance analysis, sensitivity testing, benchmarking, and ongoing model monitoring.• Prepare comprehensive model development and methodology documentation.• Partner with Credit Risk, Model Risk Management, Independent Model Validation, Finance, Data, and Technology teams.• Address model-validation findings and provide quantitative support for modeling assumptions and methodology.• Ensure models and supporting documentation meet internal governance standards and applicable regulatory expectations.Required Qualifications
• Strong hands-on experience in Credit Risk Model Development within banking or financial services.• Strong understanding of credit-risk concepts including PD, LGD, expected credit loss, and portfolio risk.• Experience developing, calibrating, validating, back-testing, and monitoring quantitative models.• Strong knowledge of statistical modeling techniques including regression, classification, segmentation, forecasting, and model-performance measurement.• Experience with Model Risk Management, Model Validation, model governance, and regulatory documentation.• Strong programming skills using Python, SAS and/or R.• Strong SQL skills and experience analyzing large financial datasets.• Ability to translate complex quantitative results into clear business and risk-management conclusions.• Strong written communication and technical documentation skills.• Experience working in highly controlled and regulated financial environments.Preferred Qualifications
• Experience with CECL, CCAR and/or regulatory stress testing.• Experience developing commercial or consumer credit-risk models.• Familiarity with banking lending products and credit lifecycle processes.• Experience working directly with independent model-validation, audit, compliance, or regulatory teams.• Advanced degree in Statistics, Mathematics, Economics, Econometrics, Data Science, Financial Engineering, or another quantitative discipline.Candidate Profile
The successful candidate should be a true risk-model developer, rather than a general Data Scientist focused primarily on AI, Generative AI, NLP, reporting, or analytics. Direct experience building and supporting credit-risk models within a banking environment is essential.