Quantitative Analytics Manager – Model Risk Management

Quantitative Analytics Manager – Model Risk ManagementLocation: Wilmington, DE (Hybrid)

OneMain is seeking a Quantitative Analytics Manager, Model Risk Management (MRM) to help lead and strengthen the firm’s Model Risk Management program in alignment with regulatory guidance, including SR 26-2 and OCC supervisory expectations. This role supports a diverse consumer credit portfolio, including Personal Loans, Credit Cards, and Automotive lending.

The position offers a unique opportunity to apply advanced analytics and machine learning expertise while exercising independent challenge across the full model lifecycle. The role also plays a key part in regulatory readiness, audit engagement, and the continued evolution of OneMain’s analytical governance framework.

Responsibilities

• Provide hands‑on model governance oversight across the full model lifecycle, including development, implementation, validation, use, and ongoing monitoring of machine learning models supporting marketing, origination, servicing, and loss mitigation.
• Perform independent and effective challenge of models, assessing conceptual soundness, data integrity, methodology, assumptions, and limitations. Evaluate key development decisions, including target construction, training versus validation strategies, sampling approaches, performance windows, hyper‑parameter tuning, model performance metrics, variable selection, and swap‑set analyses.
• Provide governance oversight of loss forecasting models
• Conduct periodic model validations and assess whether validation activities performed by internal teams or third parties meet Model Risk Management policy requirements, including outcomes analysis, benchmarking, and sensitivity testing, as appropriate.
• Apply analytics, business rules, and other risk tools to monitor model performance and behavior, identify emerging risks or anomalies, and recommend remediation or model enhancements when warranted.
• Participate in broader artificial intelligence and advanced analytics initiatives in partnership with the data science & technology organization, ensuring appropriate governance and risk controls are embedded from inception.
• Apply regression, classification, and related statistical techniques to perform deep‑dive analyses, clearly articulating both statistical and practical significance to inform risk decisions and regulatory communications.

Qualifications

• Master’s degree in a quantitative discipline (Statistics, Mathematics, Data Science, or related field) required; PhD preferred.
• 5+ years of experience in statistics, data science, decision science, or a related quantitative field.
• 3+ years of experience building, reviewing, or validating machine learning models within the consumer finance industry.
• Strong understanding of consumer lending products, credit risk practices, and regulatory expectations related to model risk management.