Odyssey
AI
HeadofDataOperations
“Head of Data Operations at Odyssey. Skills: data acquisition strategy, data partnerships, data mix and composition, data organisation scaling, data ops budget management. Define data acquisition strategy across multimodal sources. Build a diversified pipeline of vendors, publishers, robotics partners, expert networks, and custom-collected data”
What You'll Achieve.
impact on model quality and cost; scale the function to hundreds of people
Industry & Context.
Identify gaps before they hurt us; Design systems with research and engineering that surface what's missing; Turn gaps into plans quickly
What They're Looking For.
Must Have
deep, hands-on experience with multimodal data - audio, video, egocentric data capture, robotic sensor data, written or contributed to a research paper, deeply understand world models, reinforcement learning, and what research teams actually need from data, led data operations or a closely adjacent function at a frontier AI lab (OpenAI, DeepMind, Anthropic, XAI, or similar), a robotics company, or another technically demanding environment, track record of closing data deals - commercial negotiation experience with vendors, suppliers, or partners where supply was the constraint, highly autonomous, comfortable in a technical culture where everyone reads research papers and codes
Nice to Have
experience scaling a data organisation from a small team to 50+ people, background in robotics data specifically, familiarity with eval frameworks and how data composition affects benchmarks, legal/commercial fluency with data licensing
What You'll Do.
Define data acquisition strategy across multimodal sources
Build a diversified pipeline of vendors
and custom-collected data
Close and manage data partnerships
Build systems that manage relationships once they're live
Own data mix and composition
Translate model needs into concrete acquisition plans
Identify gaps before they hurt us
Design systems that surface what's missing
Build and scale the data organisation
Define operating cadence
Determine the right mix of in-house teams
and vendor partnerships
Own the data ops budget
Make trade-offs across sources
Build reporting that lets leadership see what we're spending and what we're getting for it
How You'll Work.
Team & Collaboration
Work with research and product to define the right blend of sources, domains, modalities, and quality tiers; Design systems with research and engineering that surface what's missing
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