Manager Data Science

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.

This position will be posted until filled. 

What makes you an ideal candidate?

• Advanced quantitative and analytical skills with a strong foundation in mathematics, probability, statistics, machine learning, and predictive modeling.

• Ability to translate complex business challenges into analytical solutions, formulate quantitative problem statements, and articulate business value.

• Deep knowledge of statistical and machine learning methodologies, including regression techniques, time series analysis, survival analysis, clustering, decision trees, optimization, simulation, dimensionality reduction, and other advanced analytical methods.

• Strong programming and analytical tool experience, including Python, SAS, SQL, R, JMP, and Microsoft Office applications, with a preference for experience using Python frameworks such as TensorFlow and Keras.

• Experience designing, deploying, documenting, monitoring, and maintaining analytical models and machine learning solutions, including DevOps/MLOps practices and Azure DevOps environments.

• Comprehensive knowledge of data architecture concepts, including data warehouses, data lakes, big data platforms, and large-scale dataset analysis.

• Strong written, verbal, and presentation skills with the ability to communicate complex technical concepts and insights to senior leaders and business stakeholders.

• Demonstrated curiosity, critical thinking, and problem-solving skills, with the ability to conduct independent research and develop innovative solutions to business challenges.

• Proven ability to build collaborative relationships and effectively partner with technical teams, business stakeholders, and leadership.

• Experience leading multiple projects simultaneously, prioritizing competing demands, and mentoring others in a fast-paced environment.

• Familiarity with Agile, Lean Development, and structured problem-solving methodologies.

Additional Knowledge and Skills

Working effectively within an AI enabled environment:  

• Ability to use AI tools (e.g., Microsoft Copilot) to support daily work

• Skills in evaluating AI outputs for accuracy, compliance, and bias

• Experience integrating AI into workflows to improve efficiency or insights

• Familiarity with AI assisted research, summarization, and content generation

• Understanding of responsible AI use, including ethics and data protection

Experience & Education

• 5+ years as a Data Scientist or similar quantitative field required

• 1+ years in a project leadership role required

• Master’s Degree or PhD in Statistics, Applied Mathematics, Econometrics, Economics, Operations Research, Industrial Engineering, Physics, Computer Science, or similar quantitative field Required

What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.

Our Culture: Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.

Compensation: Competitive pay and bonus eligibility.

Work Life Balance: Flexible hybrid work environment, 2-days a week in office in Fort Worth, Texas

This position is not open to agency submissions

#GMFJobs #LI-Hybrid #LI-SC1

About the role

The Manager of Data Science is the subject matter expert with an in-depth knowledge of quantitative methods and diligent knowledge of data sources and tools. The Manager of Data Science brings a strong ability for independent learning and is therefore regarded as a technical expert in the latest advances in Data Science. 

In this role you will:

• Lead the development, deployment, and maintenance of predictive, prescriptive, and statistical models across areas such as originations, collections, risk, pricing, fraud, customer experience, and marketing.

• Provide technical leadership and mentorship to Data Scientists, guiding problem formulation, model development, deployment strategies, and adherence to Data Science best practices and processes.

• Apply advanced machine learning, statistical, forecasting, optimization, and data mining techniques to solve complex business problems and drive business outcomes.

• Lead research, analysis, and modeling efforts to quantify the impact of internal and external factors on portfolio performance and key business metrics.

• Design and execute studies utilizing descriptive analytics, supervised machine learning, and advanced statistical methodologies.

• Develop and apply innovative algorithms and models to improve operations, support decision-making, and answer complex business questions.

• Coordinate project activities, provide technical oversight, and ensure analytics solutions meet business objectives and stakeholder needs.

• Partner with business leaders to identify opportunities for analytics, provide strategic recommendations, and communicate findings to senior leadership.

• Present complex analytical findings and recommendations to a variety of stakeholders through clear, concise reports and presentations.

• Lead research initiatives from project design and data collection through analysis, recommendations, and implementation.

• Provide leadership, coaching, mentoring, and technical training to Data Scientists across the organization.

• Prioritize multiple initiatives and deliver high-quality work in a fast-paced environment.