Data Partnerships Lead: Global Data Ops & Vendors

Generalist; Medford Hillside (MA); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Travel opportunities

Job summary

Generalist is seeking an experienced role focused on building and managing the data engine for embodied foundation models. You will identify data needs, sources, and partners, standing them up with the necessary hardware and governance to support scalable data collection and distribution across geographies and modalities.

You will translate model data requirements into partner-ready specs, negotiate terms, and drive the partnership from selection through scaling, while working with ML and

Qualifications

• Experience managing external data partnerships and vendors.
• Ability to translate model and data requirements into actionable partner deliverables.
• Familiarity with MSAs and SOW terms and pricing structures.

Responsibilities

• Identify data needs and map the vendor landscape across data types and quality.
• Source, evaluate, and pilot new data partners with structured trials.
• Elicit requirements by collaborating with ML and engineering leads.
• Draft partner-facing specs and define acceptance criteria and delivery units.
• Structure commercial terms and ownership of unit economics.
• Scale partnerships into a network while maintaining cost control.
• Maintain operating cadence with trackers and reviews.
• Provide feedback to research on data produced to inform next cycles.
• Travel periodically to partner sites and data collection operations.

Skills

Vendor management

Data operations

Partnership development

Contracting & SOWs

Cross-functional collaboration

Job description

Generalist is seeking an experienced role focused on building and managing the data engine for embodied foundation models. You will identify data needs, sources, and partners, standing them up with the necessary hardware and governance to support scalable data collection and distribution across geographies and modalities.

You will translate model data requirements into partner-ready specs, negotiate terms, and drive the partnership from selection through scaling, while working with ML and