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# Staff Data Scientist - AI Engineering & Productivity

- **Company:** General Motors
- **Location:** Austin, TX, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Database, Information Engineering, Cursor, Github, Python (Programming Language), Open Source Technology, Microsoft Copilot, SQL Databases, Large Language Models, Deep Learning, Generative AI, Information Technology, Databricks
- **Published:** October 9, 2026
- **Apply:** https://generalmotors.wd5.myworkdayjobs.com/Careers_GM/job/Austin-Texas-United-States-of-America/Staff-Data-Scientist---AI-Engineering---Productivity_JR-202621452

## About the Role

* 8+ years working with and 3+ years leading advanced analytics, AI/ML and/or operations research initiatives.
 * Demonstrated thought leadership surrounding use of innovative methodologies and approaches to solving business problems.
 * Deep expertise across multiple AI/ML and analytics domains (LLMs, forecasting, deep learning, operations research, prescriptive & predictive methods, etc.), with a preferred focus on LLMs.
 * Expertise in Python and other cloud-based data science platforms (Databricks preferred), and proficiency in SQL;
 * Experience with generative AI tools and platforms such as Cursor, GitHub and/or Microsoft Copilot, Glean, Databricks Genie, etc.
 * Demonstrated ability to frame ambiguous high-value business problems into tractable analytical strategies and measurable outcomes;
 * Strong presentation and communication skills.
 * M.S. degree in operations research, engineering, computer science, applied statistics, physics, or related field. Equivalent additional experience may substitute for an advanced degree., * 10+ years working with and 6+ years leading advanced analytics, AI/ML and/or operations research initiatives.
 * Ph.D. in operations research, engineering, computer science, applied statistics, physics, or related field.
 * Demonstrated ability to lead multi-team member initiatives using novel methods resulting in substantial business impact.
 * Publications, conference talks, patents or other generated intellectual property, committed open-source work, or other external technical recognition preferred.

## Description

The AI Engineering and Productivity team in the Global Planning, Design, and Product IT organization is looking for a Staff Data Scientist to propel our mission of empowering Engineering teams by delivering reliable and secure AI-driven tools that streamline workflows, accelerate decision making, surface actionable insights, and unlock measurable productivity gains across the product lifecycle.

As a Staff Data Scientist, you will be responsible for leading one or more AI/ML and data science products, while also contributing highly technical work on problems of significant complexity. You will be accountable for translating ambiguous Engineering business challenges into scalable, production-grade AI products that deliver measurable enterprise impact.

What You'll Do

 * Shape scientific direction, and ensure methodological quality and operational robustness used across the suite of products that you are responsible for.
 * Grow the technical capabilities of the scientist community within the AI Engineering and Productivity organization through exploration of new methods and technologies, cross-functional knowledge sharing, mentorship, internal reviews, and establishment of reusable methods and frameworks.
 * Drive results on time and within budget, managing risks, and ensuring methodological and operational quality across products that you oversee to generate significant and measurable business impact.
 * Work cross-functionally across data engineering, delivery teams, software delivery, other business units and beyond to drive product development, management and results; and
 * Influence stakeholders and leaders by translating technical tradeoffs, risks and opportunities into clear business decisions., This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.