AVP Data Science

Location: Fort Worth, TX, United States; Hybrid

Why GM Financial?

GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, U.S. We are a global provider of auto finance solutions, with operations in North America, South America, and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses.

At GM Financial, our team members define and shape our culture — an environment that welcomes new ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.

Our Purpose: We pioneer the innovations that move and connect people to what matters

The AVP of Data Science leads a team of data scientists in developing advanced analytics, machine learning models, and data-driven solutions that support strategic business objectives. This role oversees research initiatives, guides the application of analytics to complex business challenges, and delivers actionable insights that drive operational improvements and informed decision-making.

The AVP partners closely with business leaders to identify opportunities, communicate analytical findings, and ensure data science solutions deliver measurable value. This position combines technical leadership, people management, and strong business acumen to advance the organization's analytics capabilities and outcomes.

Lead a team of Data Scientists in developing advanced analytics, predictive models, and data-driven solutions that support key business objectives and strategic decision-making. This role partners closely with senior leadership to translate complex business challenges into analytical frameworks, deliver actionable insights, and drive operational improvements through the application of statistical modeling, machine learning, and large-scale data analysis. The ideal candidate combines strong leadership experience with deep expertise in quantitative methods, data science, and programming tools such as Python, R, SAS, and SQL. Success in this role requires exceptional problem-solving, communication, and stakeholder management skills, with the ability to present complex findings to executive audiences and influence business strategy.