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.)

Ce poste est basé à distance, mais si le candidat sélectionné habite à une distance donnée d'un centre GM, il devra se rendre sur place trois fois par semaine {ou à une autre fréquence exigée par son responsable}.

Ce poste ne donne pas droit à des indemnités de déménagement. Les éventuels frais de déménagement seront à la charge du candidat retenu.

Renseignements sur la diversité

General Motors est résolue à être un lieu de travail qui est non seulement exempt de discrimination illégale, mais aussi un endroit qui favorise véritablement l'inclusion et l'appartenance. Nous sommes convaincus que la diversité de la main-d'œuvre permet de créer un environnement dans lequel nos employés peuvent s'épanouir et développer de meilleurs produits pour nos clients. Nous encourageons les candidats intéressés à consulter les principales responsabilités et compétences requises pour chaque rôle et à postuler à tout poste qui leur correspond. Dans le cadre du processus de recrutement, les candidats peuvent devoir, le cas échéant, réussir une évaluation liée au poste ou une présélection d'emploi avant d'être embauchés. Pour en savoir plus, consultez notre processus de recrutement.

Déclaration concernant l'égalité d'accès à l'emploi (É.-U.)

General Motors est fière d'être un employeur souscrivant au principe de l'égalité d'accès à l'emploi. Tous les candidats qualifiés seront pris en compte, sans égard à la race, à la couleur, à la religion, au sexe, à l'orientation sexuelle, à l'identité de genre, à l'origine ethnique, aux situations de handicap ou au statut protégé d'ancien combattant.

Aménagements (É.-U. et Canada)

General Motors offre des occasions à tous les chercheurs d'emploi, y compris les personnes handicapées. Si vous avez besoin d'un accommodement raisonnable pour vous aider dans votre recherche d'emploi ou la soumission de votre candidature, envoyez-nous un courriel à l'adresse [email protected] ou appelez-nous au 800 865-7580. Veuillez inclure dans votre courriel une description spécifique du type d'accommodement demandé, ainsi que le titre d'emploi et le numéro de demande du poste auquel vous postulez.