Data Scientist Statistician Lead - Product Safety Data Analytics

Data Scientist Statistician Lead

The Product Safety Data Analytics team is seeking highly motivated and qualified candidates for the position of Data Scientist Statistician Lead. This is a great opportunity for an innovative data scientist/statistician and technical leader with extensive hands-on experience across the full end-to-end data science lifecycle. This technical leader role requires extensive programming skills and deep knowledge of statistical techniques to solve complex problems and provide guidance to a team of data scientists focused on advanced statistical analysis, predictive modeling, and data-driven decision support. You will be part of a highly collaborative team that values growth, creativity, and a relentless focus on business impact through analytics.

In this role, you will be responsible for leading the development of scalable solutions to identify and analyze potential emerging vehicle safety issues. You will work with business partners to understand their challenges and needs, develop appropriate statistical analyses, lead proof-of-concept efforts for new analytical capabilities, and provide technical expertise and guidance to a team of data scientists.

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We're turning today's impossible into tomorrow's standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.

Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.

Responsibilities:

• Develop and standardize best practices for continuously monitoring emerging safety issues for use in safety hazard monitoring
• Raise the bar on safety-investigation data-analytics support by continuously integrating effective analytical solutions and developing a feedback loop for tool and process improvements
• Correctly perform appropriate statistical data analyses to provide unbiased results
• Continuously drive efficiencies by implementing automation opportunities using a combination of custom software development and integration of off-the-shelf solutions
• Develop and refine statistical and machine learning models to solve complex business problems
• Prototype new analytical approaches and decision-support solutions to address business needs
• Develop workflow automation and scalable analytical tools to streamline business processes
• Identify long-term technical innovations while continuously improving execution efficiency
• Exhibit the ability to tell a succinct, data-driven story in any forum and tailor delivery to a wide range of stakeholder levels, from analyst to senior executive
• Apply strong business acumen, highly specialized knowledge, and organizational expertise to establish and advance new data-analytics support areas

Required Qualifications:

• 8+ years of work experience in applied statistics, machine learning, engineering, or data science, or 6+ years of work experience with Ph.D.
• M.S. in a quantitative discipline (Statistics, Mathematics, Econometrics, Operations Research, or other relevant degree)
• Strong background in varied statistical data analyses such as reliability analysis, analysis of variance, time series, categorical data analysis, multivariate analysis, and sampling design
• Strong background in anomaly detection, diagnostics and prognostics, and root cause analysis
• Demonstrated experience in large-scale data analytics
• Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, SQL
• Machine Learning & Advanced Analytics: supervised and unsupervised learning, natural language processing, decision trees, clustering, anomaly detection, and predictive modeling
• Data Engineering: Databricks, SQL, data pipelines, data preprocessing, and feature engineering
• Ability to effectively communicate results and methodologies; must be comfortable presenting to executive leadership
• Strong work ethic, drive for results, and the ability to work in an ambiguous workspace
• Understanding of vehicle safety technologies including design intent, function, and intended performance in the field

Preferred Qualifications:

• Ph.D. in a quantitative discipline (Statistics, Mathematics, Econometrics, Operations Research, or other relevant degree)
• 10+ years of work experience in applied statistics, machine learning, engineering, data science, or a related field
• Deep knowledge of GM's Data Ecosystem
• Deep knowledge of GM's Cloud Technology Stack for Data Science
• SME in applied reliability/survival analysis
• Experience in vehicle development or validation of safety-related systems or components
• Experience taking advanced analytics solutions from business problem statement through deployment and ongoing optimization
• Experience developing and deploying production-grade statistical or machine learning solutions that have delivered significant business value