Hiring nowDataset curation and stewardship

AI Data Steward

Also called AI Dataset Steward

An AI Data Steward controls who can use each dataset, for what, and for how long. That keeps training and evaluation data within its rights, and lets the company answer hard questions from customers, partners and regulators.

What they do

The steward keeps a register of every dataset in use: where it came from, what rights attach, who may access it and for which purposes. They run the access workflow, so requests are approved against recorded permissions rather than goodwill. They track retention limits, log dataset changes and versions, and act on correction or deletion requests. They check provenance and quality with deduplication and catalogue tools, and they work with legal, engineering and research to settle edge cases before data is used.

What a strong hire delivers

In the first months, a strong hire delivers a complete, trusted register of datasets in use, with rights and permitted uses recorded for each. They put a working access workflow in place that teams actually follow. They set clear retention rules, show that correction and deletion requests are handled on time, and flag the datasets that carry unresolved risk so leadership can decide what to do.

How you can tell they are good

Ask them to show a register or workflow they built and explain why each field exists. Give them a dataset with unclear licensing and ask what they would do before anyone trains on it. Strong people talk about evidence, owners and audit trails. Be wary of anyone who treats stewardship as a one-off clean-up or a purely legal task.

Why it is hard to fill

The title is new, so candidates rarely carry it. The work sits between data governance, information management, licensing and machine learning practice, and few people have all four. Many governance specialists have never seen a training pipeline. Many data scientists have never run a consent register or a deletion process. Companies often search by title and miss the right people.

Where to find them

Look at data governance leads, information managers, records and archive professionals, data librarians and data scientists who moved into data quality or provenance work. They come from regulated sectors, research institutions, publishers, public bodies and data-heavy product companies. Privacy operations and data catalogue teams are also good sources of people who already run these controls.

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 an AI Data Steward

  1. Tell me about a dataset register or catalogue you built or maintained. What did you record, and why?

    A strong answer names fields such as source, rights, permitted uses, owner, retention and version, and explains how each was used. Worry if the answer is a spreadsheet with no owner, no review routine or no link to access decisions.

  2. A team wants to train on a dataset whose licence is ambiguous. What do you do?

    They pause use, trace the source, read the terms, involve legal, and record the decision. They offer a safe alternative where possible. Worry about answers that say go ahead and fix it later, or block everything without engaging.

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

Job description you can post

We are hiring an AI Data Steward to take charge of how our datasets are documented, accessed and retained. You will make sure every dataset we use has clear rights, a named owner and a record of permitted use. You will work with research, engineering and legal.

What you will do

  • Maintain a stewardship register covering source, rights, permitted uses, owner and retention for each dataset
  • Run the access request workflow and approve requests against recorded permissions
  • Track dataset versions and changes, and keep an audit trail
  • Handle correction and deletion requests within agreed limits
  • Check provenance and quality, including deduplication, on incoming data
  • Raise licensing and consent questions with legal before data is used
  • Report on compliance and unresolved risks to leadership

What you will bring

  • Demonstrated work building or running a data register, catalogue or governance process
  • Practical understanding of licensing, consent and permitted use for data
  • Experience tracing data provenance and assessing quality
  • Ability to design workflows that engineers will follow
  • Clear written records and decisions others can audit
  • Comfort working with both technical and legal colleagues

Nice to have

  • Experience with machine learning training or evaluation data
  • Background in archives, information science or privacy operations
  • Experience with deduplication tools or consent registers

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

Skills that matter

  • Data quality and provenanceAdvancedTrack dataset origin, validity and transformations
  • Data stewardship and consentAdvancedMaintain 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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