Job Overview

At Microsoft AI, we are on a mission to train the world’s most capable AI frontier models, pushing the boundaries of scale, performance, and product deployment. We’re tackling some of the most challenging problems in deep learning at scale and building leading AI models across text, reasoning, and multimodal capabilities.

This role sits at the heart of our AI superintelligence lab. We’re looking for an experienced leader to drive AI data strategy and acquisition, working directly with researchers to identify the data that can unlock new model capabilities and finding creative ways to access it.

You will partner closely with AI scientists to understand emerging research needs, research and evaluate potential sources of high-value data, and develop acquisition strategies across publishers, platforms, enterprises, institutions, data providers, and other novel sources. Much of this data has never been packaged or licensed for AI training before, requiring creative sourcing, commercial judgment, and close partnership with Legal and Privacy to navigate complex and rapidly evolving questions around data rights and responsible use.

The ideal candidate is deeply curious about AI, comfortable working directly with researchers in ambiguous environments, and able to move fluidly between technical requirements, external data markets, commercial negotiations, and complex legal and policy considerations.

Job Responsibilities

Partner directly with AI researchers to understand model capabilities, research priorities, and the types and characteristics of data most likely to improve model performance.
Research global data markets and identify new, differentiated, and hard-to-access sources of training data, including content that has not historically been commercially available for AI.
Develop creative strategies to access data through licensing, partnerships, acquisitions, data creation, and other novel commercial structures.
Evaluate potential datasets and partners based on research value, uniqueness, scale, quality, rights, provenance, and usability for AI training.
Lead commercial negotiations with data owners and partners, translating research needs into practical partnership and licensing structures.
Work closely with Legal, Privacy, Compliance, and policy partners to navigate novel questions involving copyright, licensing, privacy, data rights, and responsible AI use.
Build strong relationships across the research community and external data ecosystem to anticipate emerging data needs and opportunities.
Coordinate cross-functional initiatives across Research, Business Development, Procurement, Legal, Privacy, Finance, and Engineering.
Advance the AI frontier responsibly and embody Microsoft’s culture and values.

Required Qualifications

Bachelor’s Degree AND 8+ years of experience in strategy, business development, data acquisition, technology, research partnerships, content licensing, or related fields, OR equivalent experience.
Experience working closely with technical or research teams to translate ambiguous requirements into actionable strategies or partnerships.
Demonstrated ability to research complex markets, identify unconventional opportunities, and develop strategies in areas where established playbooks do not exist.
Experience negotiating complex commercial agreements or partnerships.
Experience operating in fast-moving environments where priorities shift quickly and requirements are underspecified.
Strong judgment and ability to operate across technical, commercial, legal, and strategic considerations.
Experience building and leading a team.

Preferred Qualifications

Bachelor’s Degree AND 10+ years of experience in data strategy, business development, content/data licensing, technology strategy, research partnerships, or related fields, OR equivalent experience.
Experience working with leading AI research organizations, technology companies, data providers, publishers, platforms, or other major content owners.
Strong understanding of the emerging AI data ecosystem, including pre-training, post-training, multimodal, and specialized data.