Hiring nowDataset curation and stewardship

Training Data Strategy Lead

Also called Data Strategy Lead (Training Data) · Head of Data Acquisition

The Training Data Strategy Lead decides what data the company buys, licenses, generates or collects, and manages the vendors and rights behind it. Good decisions here shape model quality and keep legal risk low.

What they do

Works out what data the models need, then decides how to get it. That means weighing up buying, licensing, generating or collecting, and costing the trade-offs in time and risk, not just price. Runs vendor selection, negotiates terms with legal, and checks that rights cover the intended use. Sets quality and provenance standards for incoming data and designs small experiments to test whether a source helps. Keeps a clear record of where every dataset came from, what consent sits behind it and what it may be used for. Works daily with research, engineering and legal.

What a strong hire delivers

A clear map of current data holdings, their rights and their gaps. A sourcing plan tied to what the models need next, with a view on build, buy or licence. A short list of trusted vendors and partners with agreed terms. Provenance records that legal and research both rely on. First evidence from experiments showing which data sources improve results and which do not.

How you can tell they are good

Ask them to walk through a real sourcing decision: what they needed, the options, what they chose and what happened. Strong people talk about rights and quality as readily as cost. They can explain how they tested a dataset before committing. Ask what they turned down and why. Be wary of anyone who treats licensing as a legal afterthought.

Why it is hard to fill

Few people have done this at scale, and the title varies from company to company. The work sits between data science, legal, procurement and research, so candidates rarely tick every box on paper. Many have deep strength in one area and gaps in another. Demand is high and the pool is small, so good people are usually already in post at labs or well-funded model startups.

Where to find them

Look at data partnerships and licensing teams at labs and model startups, plus data governance, information science and archive backgrounds. Data scientists who moved into acquisition or vendor management are common. Publishers, media and data providers can produce people who understand rights well. Titles to search include Head of Data Acquisition, Data Partnerships Manager and Data Governance Lead.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Training Data Strategy Lead

Tell me about a decision to buy, license, generate or collect data. How did you choose, and what happened?
Strong answers set out the options, the criteria and the trade-offs, then describe the outcome honestly, including what they would change. Worry if the answer is only about price, or if they cannot say how they judged success.
How do you check the quality and provenance of a dataset before committing to it?
Look for sampling, documented lineage, checks for duplication and contamination, and small pilot tests. Good candidates ask where data came from and what consent exists. Worry about reliance on vendor claims alone.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for a Training Data Strategy Lead to decide where our model data comes from and make sure we have the right to use it. The work sits between research, engineering and legal, and shapes both the quality of our models and the risk we carry.

What you will do

Decide whether to buy, license, generate or collect data for each need
Select and manage data vendors and partners
Negotiate data terms with legal and commercial teams
Set standards for data quality, provenance and consent
Design experiments to test the value of new data sources
Maintain clear records of dataset origin and permitted use
Report data gaps and plans to research and leadership

What you will bring

Demonstrated experience sourcing or licensing data for machine learning
Working knowledge of intellectual property and data licensing terms
Experience assessing data quality and tracing provenance
Ability to design and read simple experiments
Track record managing vendors or partners
Clear communication with researchers and lawyers

Nice to have

Background in data governance, archives or information science
Experience with consent and data stewardship practices
Familiarity with synthetic data generation

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

Data quality and provenanceWorkingTrack dataset origin, validity and transformations
Data stewardship and consentWorkingMaintain permissions, retention and responsible data access
Experimental designWorkingChoose comparisons that identify credible effects
Intellectual property and licensingWorkingAssess rights in training inputs and generated outputs

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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