AI Roles

Dimension	AI Worker	AI Professional	AI Leader
Dimension A: Privacy and stewardship	Working	Practitioner or Expert	Expert
Dimension B: Specification, acquisition, engineering, architecture, storage and curation	Awareness or Working	Working, Practitioner or Expert	Working, Practitioner or Expert
Dimension C: Problem definition and communication	Awareness or Working	Practitioner-Expert	Expert
Dimension D: Problem solving, analysis, modelling, visualisation	Awareness or Working	Working, Practitioner or Expert	Working, Practitioner or Expert
Dimension E: Evaluation and reflection	Working	Practitioner-Expert	Expert

Dimension A: Privacy and Stewardship

This area concerns the security and protection of data, including the design, creation, storage, distribution and associated risks.

This dimension is concerned with:

• The data life cycle (provenance, identification, management, analysis, exploitation, curation).

• Data stewardship and standards.

• Knowledge and understanding of information security.

• Protection and management of personal and sensitive data.

• Knowledge of the legal and regulatory environment.

Dimension B: Specification, acquisition, engineering, architecture, storage and curation

This area concerns the collection, secure storage, manipulation, and curation of data, as well as the application of data management and analytical techniques.

• Competence within this dimension will be underpinned by engagement in domain- and/or sector-specific knowledge, and the ability to frame a technical response within these contexts and constraints.

• Awareness and activity around practical data controls will articulate fully with legal, regulatory and ethical considerations. The complexities of handling situations arising from the (mis)use of sensitive data will be reflected in the proposal of practical solutions.

Dimension C: Problem definition and communication

This area concerns the ability to identify and clearly define a problem with others and communicate solutions to both technically qualified and lay audiences.

• Individuals should be able to demonstrate an understanding of the wider context within which organisations work.

• They should understand the contribution data science and artificial intelligence can make to their objectives, and the impact of dimensions of data maturity on an organisation’s ability to progress.

• They should be able to demonstrate communication skills, including written, oral and visual.

• They will possess the ability to communicate technical knowledge, including both substantive results, the procedures used to create them, and the uncertainty or limitations inherent in them, and to do so in a manner appropriate to the nature of the audience.

Dimension D: Problem solving, analysis, modelling, visualisation

This area concerns the knowledge of and ability to apply a range of mathematical, statistical and computing tools and methods to define and analyse a problem and present solutions.

• Individuals should be able to demonstrate experience in undertaking data analysis, including (but not limited to) exploratory data analysis, visualisation and predictive work.

• They will be aware of the opportunities as well as limitations of various data and AI methods, and will be mindful of constraints.

Dimension E: Evaluation and reflection

This area concerns reflecting on performance and outcomes, identifying development needs and applying important principles associated with ethics and sustainability.

• It is important that all professionals working within the field of data science and artificial intelligence have a clear understanding of the ethics that underpin the collection, management, use, and communication of the data and the results with which they work.

• It is equally important that a data professional takes responsibility for the assurance of the models they build.

• Assurance covers both the efficacy of the application and the ethical nature of its design and implementation.