Staff Data Scientist - AI Engineering & Productivity

Description

The AI Engineering and Productivity team sits inside the Global Planning, Design, and Product IT organization. It builds AI-driven tools for Engineering teams that are reliable and secure, cut down on manual steps, speed up decisions, and produce useful insights that improve productivity across the product lifecycle.

The company is hiring a Staff Data Scientist to lead one or more AI/ML and data science products while doing hands-on technical work on hard problems. The role turns unclear Engineering business problems into scalable, production-ready AI products with measurable effect across the enterprise.

Responsibilities

• Lead one or more AI/ML and data science products and remain hands-on with complex technical work.
• Turn vague Engineering business problems into production-grade AI products that scale and show measurable enterprise value.
• Set the scientific direction for your products and make sure their methods and operations are sound.
• Build up the scientists in the organization by testing new methods and tools, sharing knowledge, mentoring, running internal reviews, and creating reusable methods and frameworks.
• Deliver work on schedule and within budget, manage risk, and keep methods and operations at a high standard across the products you oversee, aiming for significant, measurable business results.
• Collaborate with data engineering, delivery teams, software delivery, other business units, and other groups to build, manage, and deliver products.
• Explain technical tradeoffs, risks, and opportunities to stakeholders and leaders so they can make clear business decisions.

Requirements

• 8+ years working on advanced analytics, AI/ML, and/or operations research initiatives, with at least 3+ years in a leadership role over such work.
• Show clear thought leadership in applying new methods and approaches to business problems.
• Bring deep experience across several AI/ML and analytics areas, such as LLMs, forecasting, deep learning, operations research, and prescriptive and predictive methods, with a preference for LLM work.
• Be proficient in Python and cloud-based data science platforms (Databricks preferred), and skilled in SQL.
• Have hands-on experience with generative AI tools such as Cursor, GitHub and/or Microsoft Copilot, Glean, and Databricks Genie.
• Take ambiguous, high-value business problems and shape them into workable analytical plans with measurable outcomes.
• Communicate clearly and present well.
• Hold an M.S. in operations research, engineering, computer science, applied statistics, physics, or a related field. Equivalent experience may be accepted in place of an advanced degree.
• Preferred: 10+ years working on advanced analytics, AI/ML, and/or operations research initiatives, with 6+ years leading them.
• Preferred: a Ph.D. in operations research, engineering, computer science, applied statistics, physics, or a related field.
• Preferred: experience leading initiatives across several team members using novel methods that create substantial business impact.
• Preferred: publications, conference talks, patents or other intellectual property, committed open-source work, or other external technical recognition.

Conditions

The role is hybrid: you are expected to work from a specific location at least 3 times a week, or at a frequency your manager sets.

The company does not provide immigration sponsorship for this role. Do not apply if you will now or in the future need company immigration sponsorship, including company sponsorship, the company acting as employer of record on a government form, or any work authorization that needs a written submission or other immigration support from the company (such as H-1B, OPT, STEM OPT, CPT, TN, or J-1).

Applicants may be required to complete a role-related assessment and/or a pre-employment screening before starting work.

The company is committed to a workplace free of unlawful discrimination that also supports inclusion and belonging.

Benefits

• Relocation benefits are available to candidates who qualify under company policy.