Quantitative Analytics Manager – Model Risk Management

Location: 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 11‑7 and OCC supervisory expectations. This role supports a diverse consumer credit portfolio, including Personal Loans, Credit Cards, Automotive, and Point‑of‑Sale 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 Fair Lending 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 robust challenge and governance oversight of CECL and loss forecasting models, serving as a central point of contact for internal audit, external audit, and regulatory examinations. Prepare clear, well‑supported model governance and validation documentation in support of model approvals and ongoing use.
• 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.
• Contribute to the ongoing modernization of the MRM function by leveraging advanced analytics and AI‑enabled tools to improve governance efficiency, documentation quality, and knowledge management.
• 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.
• Support OneMain’s Fair Lending Analytics Program by developing fair lending models and conducting statistically rigorous analyses to assess potential disparate impact and compliance risk.
• 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.
• Hands‑on experience with machine learning techniques, particularly tree‑based models such as XGBoost, and strong analytical “deep‑dive” capabilities.
• Exposure to modern AI concepts, including Generative AI, Large Language Models (LLMs), and Retrieval‑Augmented Generation (RAG) systems, with an appreciation for associated governance and risk considerations.
• Proven ability to lead and manage complex, ambiguous projects and provide structured, defensible analytical judgment.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts to non‑technical stakeholders, auditors, and regulators.
• Demonstrated intellectual curiosity, strong idea generation, and an interest in research, innovation, and continuous improvement.
• Proficiency in Python and SQL; experience with AWS and SageMaker is a strong plus.
  
  

Who We Are

OneMain Financial (NYSE: OMF) is the leader in offering nonprime customers responsible access to credit and is dedicated to improving the financial well-being of hardworking Americans. Since 1912, we’ve looked beyond credit scores to help people get the money they need today and reach their goals for tomorrow. Our growing suite of personal loans, credit cards and other products help people borrow better and work toward a brighter future.

Driven collaborators and innovators, our team thrives on transformative digital thinking, customer-first energy and flexible work arrangements that grow lives, careers and our company. At every level, we’re committed to an inclusive culture, career development and impacting the communities where we live and work. Getting people to a better place has made us a better company for over a century. There’s never been a better time to shine with OneMain.

Benefits

Because team members at their best means OneMain at our best, we provide opportunities and benefits that make their health and careers a priority. That’s why we’ve packed our comprehensive benefits package for full- and some part-timers with:

• Health and wellbeing options including medical, prescription, dental, vision, hearing, accident, hospital indemnity, and life insurances
• Up to 4% matching 401(k)  
• Employee Stock Purchase Plan (10% share discount)  
• Tuition reimbursement  
• Paid time off (15 days’ vacation per year, plus 2 personal days, prorated based on start date)
• Paid sick leave as determined by state or local ordinance, prorated based on start date
• Paid holidays (11 days per year, based on start date)
• Paid volunteer time (3 days per year, prorated based on start date)