Vice President - Risk

Lobel Financial•Anaheim, CA

1d•$200,000 - $275,000•Hybrid

About The Position

The Vice President of Risk will lead Lobel Financial's enterprise risk function and modernize how the company measures, predicts, and manages risk. The role owns credit risk strategy, risk analytics and modeling, portfolio performance, and the risk technology stack. The successful candidate will combine deep consumer lending experience with hands-on knowledge of modern risk technologies, including machine learning models, alternative data, cloud-based analytics, and automated decisioning, while balancing growth and loss performance and maintaining strong governance and regulatory compliance. The Vice President will set the vision and multi-year roadmap for the risk function, lead and develop teams across credit risk, analytics, modeling, and risk operations, and advise executives and the Board on risk appetite, loss forecasting, and portfolio strategy. The position owns underwriting strategy, credit policy, approval criteria, and portfolio monitoring, and partners with Collections and Servicing on loss mitigation and recovery. The role leads the adoption of modern risk tools and modernization of risk data and analytics infrastructure in partnership with IT and Data teams. It also introduces champion/challenger testing and rapid experimentation, strengthens fraud detection, maintains model risk management, and ensures models and policies support fair lending, explainability, and adverse action requirements. The Vice President partners with Compliance and Legal on regulatory matters and works across Sales, Dealer Relations, Operations, and Finance to balance growth with credit quality and communicate risk insights clearly. This hybrid position is based at Lobel Financial's headquarters in Anaheim, California, and reports up through the Chief Investment Officer. Compensation is a base salary of $200,000 to $275,000, plus bonus eligibility.

Requirements

Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related quantitative field.
12+ years of experience in consumer lending risk management, including at least 5 years in a senior leadership role.
Proven track record of modernizing a risk function, such as deploying machine learning models, new data sources, or automated decision engines into production.
Deep knowledge of credit scoring, underwriting strategy, loss forecasting, and portfolio analytics.
Working knowledge of modern analytics tools and platforms, such as Python, R, SQL, SAS, and cloud data environments.
Strong understanding of model risk management and consumer lending regulation, including fair lending and adverse action requirements.
Demonstrated ability to lead change in an established organization and build buy-in across teams.
Excellent communication skills and experience presenting to executive leadership and Boards.

Nice To Haves

Experience in auto finance, including indirect lending through a dealer network.
Subprime or non-prime auto lending experience and strong understanding of the risk dynamics of this segment.
Familiarity with auto-specific risk drivers, including deal structure, LTV, payment-to-income, vehicle valuation, dealer performance, repossession, and remarketing.
Experience with dealer risk monitoring and fraud prevention in auto lending.
Advanced degree (MS, MBA, or PhD) in a quantitative or business discipline.
Experience leading a risk technology migration or vendor selection for decisioning, data, or modeling platforms.

Responsibilities

Set the vision and multi-year roadmap for the risk function, aligned with company modernization goals.
Lead, develop, and grow teams across credit risk, analytics, modeling, and risk operations.
Advise the executive team and Board on risk appetite, loss forecasting, and portfolio strategy.
Own underwriting strategy, credit policy, and approval criteria across the full credit spectrum.
Monitor portfolio performance, including delinquency, charge-offs, roll rates, recoveries, and vintage trends.
Partner with Collections and Servicing to optimize loss mitigation and recovery strategies.
Lead adoption of machine learning and AI-driven scorecards, alternative and bureau-plus data, and real-time decision engines.
Modernize risk data and analytics infrastructure with IT and Data teams, including cloud platforms and automation.
Introduce champion/challenger testing and rapid experimentation to improve decisions.
Strengthen detection of identity, synthetic, and income/employment fraud.
Maintain model risk management, including validation, monitoring, and documentation.
Ensure models and policies support fair lending, explainability, and adverse action requirements.
Partner with Compliance and Legal on regulatory exams, audits, and evolving consumer finance regulation.
Work with Sales, Dealer Relations, Operations, and Finance to balance volume growth with credit quality.
Translate complex risk insights into clear recommendations for business leaders.