Staff Data Scientist

Job Description Mission Turn complex business questions and high-value data into trustworthy, production-grade machine-learning solutions that improve decisions, automate work, and create measurable business impact across Sales, Service, Marketing, and Global Markets. This is a hands-on Staff Data Scientist role for an experienced individual contributor who can move seamlessly from business problem framing and analytical discovery to feature engineering, model development, production deployment, and continuous improvement. The role combines deep technical expertise with strong business judgment, helping teams adopt rigorous, interpretable, and reusable data-science practices at scale. Key Responsibilities Applied Machine Learning Translate ambiguous business problems into clear analytical objectives, modeling strategies, and measurable success criteria. Develop, validate, and improve predictive, prescriptive, forecasting, optimization, classification, and segmentation models. Select appropriate statistical and machine-learning techniques based on the business decision, available data, operational constraints, and expected value. Apply advanced methods such as time-series forecasting, causal inference, experimentation, natural-language processing, and optimization when they are fit for purpose. Data and Feature Engineering Define data requirements and partner with data engineering and business teams to establish reliable, well-documented data sources. Build scalable, reproducible feature pipelines and reusable analytical assets. Perform exploratory analysis, data-quality assessment, feature selection, and leakage detection to ensure models are based on sound data. Work across structured and unstructured data, including customer, vehicle, dealer, sales, service, warranty, incentive, and operational datasets. Model Evaluation and Decision Quality Establish rigorous evaluation frameworks that reflect real-world business outcomes, not only offline technical metrics. Assess model performance, calibration, bias, interpretability, robustness, and operational fit. Explain model behavior, assumptions, limitations, and recommendations clearly to technical and nontechnical stakeholders. Design and analyze experiments, pilots, and champion/challenger approaches to validate value before broad adoption. Production ML and MLOps Package and deploy models as reliable production services, batch processes, or decision-support capabilities in partnership with software, data, and platform engineers. Establish reproducible practices for dependency management, versioning, data lineage, experiment tracking, and model release management. Design model monitoring for accuracy, data quality, drift, latency, availability, and business performance. Define practical drift thresholds, automated alerts, retraining criteria, and service-level expectations for models operating in production. Investigate production issues, identify root causes, and improve models and pipelines through structured iteration. Business Partnership and Delivery Collaborate with product leaders, business owners, architects, engineers, IT, Finance, and other partners to deliver end-to-end solutions. Connect technical work to measurable outcomes such as revenue growth, cost reduction, productivity, customer experience, risk reduction, or improved operational decisions. Balance analytical sophistication with usability, speed to value, maintainability, and adoption. Lead the data-science workstream from concept through production and continuous improvement, maintaining clear documentation and delivery accountability. Technical Leadership and Enablement Serve as a technical authority and trusted advisor on machine learning, statistical modeling, experimentation, and production data science. Raise the quality bar for model development through reusable patterns, code reviews, documentation, testing, and reproducibility. Coach data scientists, analysts, engineers, and citizen builders on sound modeling practices and responsible use of AI. Help teams evaluate and use platforms such as Databricks, Azure AI, Glean, and other enterprise tooling when they accelerate delivery without compromising quality. Share lessons learned, reusable components, and practical guidance across the AI Center and partner organizations. Required Qualifications Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field; advanced degree preferred. 8+ years of professional experience in data science, machine learning, applied statistics, or a closely related discipline. Demonstrated experience taking machine-learning solutions from problem definition and proof of concept through production deployment and ongoing operation. Strong proficiency in Python and SQL, including experience with production-quality code, testing, version control, and documentation. Strong hands-on experience with common data-science and machine-learning libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, or equivalent technologies. Experience with feature engineering, model evaluation, experiment design, statistical analysis, and communicating results to nontechnical audiences. Experience deploying models through APIs, batch pipelines, notebooks-to-production workflows, or comparable production patterns. Practical understanding of MLOps, including experiment tracking, model versioning, data and model monitoring, drift detection, retraining, and release management. Experience working with large-scale data platforms such as Databricks, Spark/PySpark, cloud data warehouses, or equivalent technologies. Demonstrated ability to operate independently, make sound technical tradeoffs, and deliver in a fast-changing, cross-functional environment. Preferred Qualifications Master’s or PhD in Statistics, Computer Science, Machine Learning, Operations Research, Mathematics, or a related quantitative field. Experience in automotive, sales, service, marketing, customer analytics, dealer analytics, warranty, incentives, forecasting, or other operationally complex domains. Experience with causal inference, time-series forecasting, optimization, recommendation systems, natural-language processing, or generative-AI-enabled analytical workflows. Experience with MLflow or comparable tools for experiment tracking, model registry, and lifecycle management. Experience with Azure, Databricks, REST APIs, containerized deployment, CI/CD, and cloud-native data or ML services. Experience defining model governance, responsible-AI controls, interpretability practices, or risk-based evaluation standards. Experience quantifying financial impact and partnering with Finance or business leaders to validate value realization. Familiarity with enterprise AI platforms, including Glean, Azure AI Foundry, Databricks, or comparable platforms. Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate. The salary range for this role is $160,000-$246,000 . The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more #LI-HP2 GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc). This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate. About GM Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all. Why Join Us We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team. Benefits Overview From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources . Non-Discrimination and Equal Employment Opportunities (U.S.) General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers. 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