BMG Money
Vice President of Credit Decision Sciences
Title:
Vice President of Credit Decision Sciences Reports to:
Chief Credit Risk Officer Location:
Remote Overview
At BMG Money, our mission is to provide access to affordable and responsible credit for underserved consumers facing unexpected expenses. We redefine lending through technology, enabling underserved individuals to thrive financially through forward-thinking, responsible, and innovative financial solutions. Job Summary
We are seeking a Vice President of Data Science to lead the development of credit risk and fraud models, manage a high-performing data science team, and foster strong partnerships with banks. This role provides data and analytic support to the credit risk strategy teams as well as credit policy and oversight functions. The ideal candidate will have deep experience in credit risk modeling in the subprime lending sector, track record in team-building, and success collaborating with partner banks to optimize credit portfolios and ensure regulatory compliance. Key Responsibilities
Lead the design, development, validation and monitoring of credit risk and fraud models tailored for the subprime lending sector. Build, mentor, and manage a high-performing team of data scientists, quantitative analysts, and risk modelers. Evaluate new data sources for possible use in models, strategies, or consumer insights. Collaborate with underwriting, collections, compliance, and senior leadership to align models and data with business objectives. Establish and maintain strong working relationships with partner banks, ensuring mutual understanding of risk policies and model standards. Monitor and improve model performance through rigorous validation, backtesting and model governance. Ensure compliance with regulatory standards and internal risk governance policies. Monitor industry trends, regulatory changes, and emerging best practices in credit risk modeling and data science. Qualifications
10+ years of experience in credit risk modeling and data science within the lending industry, preferably in subprime or non-prime segments. Proven leadership experience in building and managing analytics teams. Ability to build and manage high-functioning remote teams. Strong expertise in statistical modeling, machine learning, and advanced analytics. Familiarity with modern data platforms, Python, R, SQL, and cloud-based analytic tools. Deep understanding of credit risk frameworks, regulatory requirements (e.g., FDIC, CFPB, OCC), and model validation standards and governance. Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders. Strategic thinker with strong problem-solving and decision-making skills. Preferred Qualifications
Demonstrated success in operating within partner bank relationships. Experience with payroll-related lending and risk management. Experience using cash flow data in modeling and underwriting. Educational Background
Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related field required. Master's degree or higher (e.g., PhD, MBA) strongly preferred. Seniority level
Executive
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Title:
Vice President of Credit Decision Sciences Reports to:
Chief Credit Risk Officer Location:
Remote Overview
At BMG Money, our mission is to provide access to affordable and responsible credit for underserved consumers facing unexpected expenses. We redefine lending through technology, enabling underserved individuals to thrive financially through forward-thinking, responsible, and innovative financial solutions. Job Summary
We are seeking a Vice President of Data Science to lead the development of credit risk and fraud models, manage a high-performing data science team, and foster strong partnerships with banks. This role provides data and analytic support to the credit risk strategy teams as well as credit policy and oversight functions. The ideal candidate will have deep experience in credit risk modeling in the subprime lending sector, track record in team-building, and success collaborating with partner banks to optimize credit portfolios and ensure regulatory compliance. Key Responsibilities
Lead the design, development, validation and monitoring of credit risk and fraud models tailored for the subprime lending sector. Build, mentor, and manage a high-performing team of data scientists, quantitative analysts, and risk modelers. Evaluate new data sources for possible use in models, strategies, or consumer insights. Collaborate with underwriting, collections, compliance, and senior leadership to align models and data with business objectives. Establish and maintain strong working relationships with partner banks, ensuring mutual understanding of risk policies and model standards. Monitor and improve model performance through rigorous validation, backtesting and model governance. Ensure compliance with regulatory standards and internal risk governance policies. Monitor industry trends, regulatory changes, and emerging best practices in credit risk modeling and data science. Qualifications
10+ years of experience in credit risk modeling and data science within the lending industry, preferably in subprime or non-prime segments. Proven leadership experience in building and managing analytics teams. Ability to build and manage high-functioning remote teams. Strong expertise in statistical modeling, machine learning, and advanced analytics. Familiarity with modern data platforms, Python, R, SQL, and cloud-based analytic tools. Deep understanding of credit risk frameworks, regulatory requirements (e.g., FDIC, CFPB, OCC), and model validation standards and governance. Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders. Strategic thinker with strong problem-solving and decision-making skills. Preferred Qualifications
Demonstrated success in operating within partner bank relationships. Experience with payroll-related lending and risk management. Experience using cash flow data in modeling and underwriting. Educational Background
Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, or a related field required. Master's degree or higher (e.g., PhD, MBA) strongly preferred. Seniority level
Executive
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