[Skip To Content]

Staff Data Scientist

  • 위치
    • Warren, Michigan
    • WFH_City, District of Columbia
  • 직무 유형 Full time
  • 게시됨
  • Job Requisition JR-202617746

설명

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.)

이 직무는 재택 기반이지만, 선발된 지원자가 GM 허브에서 특정 거리 이내에 거주하는 경우 주 3회 {또는 관리자가 지정한 다른 빈도로} 출근해야 합니다.

이 직무는 리로케이션 혜택을 받을 수 없습니다. 모든 리로케이션 관련 비용은 최종선정 된 지원자가 부담해야 합니다.

다양성 정보

General Motors는 법적으로 금지된 차별을 배제하는 것은 물론 포용성과 소속감을 진정으로 장려하는 직장이 되기 위해 노력하고 있습니다. 당사는 다양성이 보장되는 환경에서 직원들이 역량을 발휘하고 우리 고객을 위한 더 좋은 제품을 개발할 수 있다고 믿습니다. 따라서 입사에 관심 있는 사람이 있다면 포지션별 주요 업무와 자격을 확인하고 본인이 보유한 기술과 능력에 부합하는 모든 포지션에 적극적으로 지원하기를 장려합니다. 지원자는 채용 과정에서 역할 관련 평가(해당하는 경우) 및/또는 채용 전 스크리닝을 통과해야 합니다.  자세한 정보는 GM 채용 과정 안내를 참고하십시오.

공평한 취업 기회 선언 (미국)

General Motors는 공평한 기회를 제공하는 고용주임을 자부합니다.  자격을 만족하는 지원자는 인종과 피부색, 성별, 성적 지향, 성별 정체성, 국적, 장애, 재향 군인 보호법 적용 여부와 상관없이 채용 후보로서 심사를 받습니다. 

숙소 (미국 및 캐나다)

General Motors는 장애인을 포함한 모든 구직자들에게 취업 기회를 제공합니다. 구직이나 취업 지원에 도움이 되는 합리적인 숙소가 필요한 경우 [email protected]으로 이메일을 보내시거나 800-865-7580으로 전화주십시오. 이메일에, 귀하가 요청하는 특정한 숙소에 대한 설명과 귀하가 지원하는 직무와 채용 요청서 번호를 포함해주세요.