Staff Data Scientist - AI Engineering & Productivity

General Motors Corporation

Austin, Northern (TX, KY)

Hybrid

USD 140,000 - 190,000

Full time

3 days ago
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Benefits offered by this job

Hybrid work model
Relocation benefits

Job summary

General Motors Corporation is seeking a Staff Data Scientist for the AI Engineering and Productivity team. You will lead AI/ML product initiatives, translate complex engineering problems into scalable AI solutions, and drive measurable productivity gains across product lifecycles.

The role requires extensive analytics experience and strong Python/SQL skills, with Databricks and generative AI tools as advantageous. This hybrid position aligns with Austin, TX and Warren, MI locations.

Qualifications

  • 8+ years of analytics experience with 3+ years leading AI/ML or operations research initiatives.
  • Deep expertise across AI/ML and analytics domains (LLMs, forecasting, deep learning, prescriptive & predictive methods).
  • Strong Python and SQL skills; Databricks experience preferred; familiarity with generative AI tools.

Responsibilities

  • Shape scientific direction and ensure methodological quality and robustness across products.
  • Grow the scientist community through mentorship and reusable frameworks.
  • Deliver results on time and within budget while managing risks and impact.
  • Collaborate across data engineering, software delivery, and business units to drive product development.
  • Translate technical tradeoffs into clear business decisions.

Skills

Advanced analytics
AI/ML
Operations research
Python
SQL
Communication
Presentations

Education

MS in operations research or related field
Equivalent experience

Tools

Databricks
Cursor
GitHub Copilot
Glean
Databricks Genie

Job description

## Staff Data Scientist - AI Engineering & ProductivityApply: Hybrid: Austin, Texas, United States of America: Warren, Michigan, United States of America: Full time: Posted Today: JR-202621452**Job Description**## **The Role**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.## **Your Skills & Abilities (Required Qualifications)*** **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.## **What Can Give You a Competitive Advantage (Preferred Qualifications)*** 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.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 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}.Relocation benefits are available for candidates who qualify under company policy.
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