Quantitative Analytics Manager – Model Risk Management

Quantitative Analytics Manager – Model Risk Management

Location: Wilmington, DE (Hybrid)

About the role

The role supports a diverse consumer credit portfolio, including Personal Loans, Credit Cards, and Automotive lending.

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.
• Hands-on experience with machine learning techniques, particularly tree-based models such as XGBoost, and strong analytical "deep-dive" capabilities.
• 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.
• Experience with analytical fair lending models and analysis is a strong plus
• Proficiency in Python and SQL; experience with AWS and SageMaker is a strong plus.

Who We Are

We foster an entrepreneurial spirit that's powered by a national brand – our teams are empowered to make a difference. We encourage teams to take ownership of initiatives in this fast-paced, innovative culture so they can drive solutions that stay ahead of customer needs. We prioritize teamwork and building in-person connections with each other, understanding that fostering a collaborative environment is the best way to support each other. We promote avenues to allow team members to expand their professional capabilities and continuously develop skills, facilitating upward mobility and career progression.

Benefits

• We promote social and family well-being by offering paid time off for volunteer hours and providing family back-up care.
• We offer extensive, comprehensive coverage to support team members’ needs physically and mentally, such as access to Talkspace and Hinge for on-demand physical therapy via an app.
• We offer financial wellness that includes 401(k) with match, ESPP, tuition reimbursement and tools like subscription cancelation that help you stay on top of your financial goals.