Manager Data Science

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.

Responsibilities

• 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.

Qualifications

• 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.

Benefits

• 401K matching
• Bonding leave for new parents (12 weeks, 100% paid)
• Tuition assistance
• Training
• GM employee auto discount
• Community service pay
• Nine company holidays

Pay

Competitive pay and bonus eligibility.

Schedule

Flexible hybrid work environment, 2-days a week in office in Fort Worth, Texas.