Artificial intelligence (AI) data specialist

Level 6/7 apprenticeship (non-degree qualification) · Digital route · Standard ST0763 v1.0

Level: 7
Typical duration: 24 months (about 2 years)
Max funding band: £17,000
Route: Digital

Discover new artificial intelligence solutions that use data to improve and automate business processes.

About the role

This occupation is found in any sector or organisation that analyses high-volume or complex data sets using advanced computational methods, such as Agriculture, Environmental, Business, Leisure, Travel, Hospitality, Education, Public Services, Construction, Creative and Design, Media, Engineering, Technology, Manufacturing, Health, Science, Legal, Finance, Accountancy, Sales, Marketing, Procurement, Transport and Logistics

The broad purpose of the occupation is to discover and devise new data-driven AI solutions to automate and optimise business processes and to support, augment and enhance human decision-making. AI Data Specialists carry out applied research in order to create innovative data-driven artificial intelligence (AI) solutions to business problems within the constraints of a specific business context. They work with datasets that are too large, too complex, too varied or too fast, that render traditional approaches and techniques unsuitable or unfeasible.

AI Data Specialists champion AI and its applications within their organisation and promote adoption of novel tools and technologies, informed by current data governance frameworks and ethical best practices.

They deliver better value products and processes to the business by advancing the use of data, machine learning and artificial intelligence; using novel research to increase the quality and value of data within the organisation and across the industry. They communicate, internally and externally, with technology leaders and third parties.

In their daily work, an employee in this occupation interacts with a broad spectrum of people and collaborates with, and provides technical authority and insight to, a diverse business community of Senior Leaders Data Scientists, Data Engineers, Statisticians, Analysts, Research and Development Scientists and Academics. Their interactions extend to working externally alongside other organisations, such as local and international governments, businesses, policy regulators, academic research scientists and non-technical audiences. They will work independently and collaboratively as required, reporting to Heads of Data, Chief Architects, Company Directors, Product Managers and senior decision makers within any organisation.

An employee in this occupation will be responsible for initiating new projects in an agile environment, and collaboratively maintaining technical standards within AI solutions applied across the organisation and its customers. They lead research into AI and its potential application within the business. They collaborate with and influence policy and operations teams to identify areas where AI solutions can create new business opportunities and efficiencies.

How it compares

At 24 months (about 2 years), it is shorter than average for a degree-level apprenticeship (the average across all 172 Level 6+ standards is 39 months).

The funding band — the maximum an employer's levy can contribute to training costs — is £17,000, below the £21,500 average for degree-level standards. Apprentices never pay tuition either way.

Typical job titles

Machine learning engineer
Artificial intelligence engineer
Director AI
AI strategy manager
Artificial intelligence specialist
Machine learning specialist

What you'll learn

The official standard defines 15 core duties, 29 knowledge areas, 28 skills, 8 behaviours. This is the actual assessed content of the apprenticeship — worth reading before an interview, because competency questions are usually written straight from these lists.

Core duties 15

Initiate new projects in an agile environment, and collaboratively maintain technical standards within AI solutions applied across the organisation and its customers.
Critically evaluate and synthesise research findings in AI and related fields and translate into organisational context.
Use the conclusions drawn from applied research in order to develop innovative, scalable data-driven AI solutions for business problems
Contribute to the development and ethical and legal conduct of AI systems and processes, in line with organisational and regulatory requirements.
Investigate and devise the most efficient and effective architectures, to enable and maximise the use and impact of AI systems and solutions for the organisation.
Develop innovative approaches to tackle known business problems that previously did not have a feasible solution within the constraints of a specific business context.
Initiate and design scalable batch/real-time analytical solutions to business problems leveraging AI and related technologies such as, data science, machine learning and statistics and related technologies.
Enhance awareness of the wider application of AI tools and technologies across the business so that opportunities for its use can be identified
Develop and architect new robust data sourcing and processing systems to serve the organisation.
Design technical roadmaps for data life-cycles ensuring appropriate support and business processes are in place.
Create and optimise efficient mechanisms for accessing and analysing datasets that are too large, too complex, too varied or too fast, that render traditional approaches and techniques unsuitable or unfeasible, in order to deliver business outcomes
Identify best practice in AI data systems, data structures, data architecture and data warehousing technologies and provide technical oversight in order to meet business objectives.
Assess risks/limitations and quantify biases associated with applications of AI within given business contexts.
Provide technical authority for the business regarding emerging opportunities for AI.
Practice continuous self-learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development

Knowledge 29

How to use AI and machine learning methodologies such as data-mining, supervised/unsupervised machine learning, natural language processing, machine vision to meet business objectives
How to apply modern data storage solutions, processing technologies and machine learning methods to maximise the impact to the organisation by drawing conclusions from applied research
How to apply advanced statistical and mathematical methods to commercial projects
How to extract data from systems and link data from multiple systems to meet business objectives
How to design and deploy effective techniques of data analysis and research to meet the needs of the business and customers
How data products can be delivered to engage the customer, organise information or solve a business problem using a range of methodologies, including iterative and incremental development and project management approaches
How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance and usability testing
How to interpret organisational policies, standards and guidelines in relation to AI and data
The current or future legal, ethical, professional and regulatory frameworks which affect the development, launch and ongoing delivery and iteration of data products and services.
How own role fits with, and supports, organisational strategy and objectives
The roles and impact of AI, data science and data engineering in industry and society
The wider social context of AI, data science and related technologies, to assess business impact of current ethical issues such as workplace automation and misuse of data
How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace
The business value of a data product that can deliver the solution in line with business needs, quality standards and timescales
The engineering principles used (general and software) to investigate and manage the design, development and deployment of new data products within the business
Understand high-performance computer architectures and how to make effective use of these
How to identify current industry trends across AI and data science and how to apply these
The programming languages and techniques applicable to data engineering
The principles and properties behind statistical and machine learning methods
How to collect, store, analyse and visualise data
How AI and data science techniques support and enhance the work of other members of the team
The relationship between mathematical principles and core techniques in AI and data science within the organisational context
The use of different performance and accuracy metrics for model validation in AI projects
Sources of error and bias, including how they may be affected by choice of dataset and methodologies applied
Programming languages and modern machine learning libraries for commercially beneficial scientific analysis and simulation
The scientific method and its application in research and business contexts, including experiment design and hypothesis testing
The engineering principles used (general and software) to create new instruments and applications for data collection
How to communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
The need for accessibility for all users and diversity of user needs

Skills 28

Use applied research and data modelling to design and refine the database & storage architectures to deliver secure, stable and scalable data products to the business
Independently analyse test data, interpret results and evaluate the suitability of proposed solutions, considering current and future business requirements
Critically evaluate arguments, assumptions, abstract concepts and data (that may be incomplete), to make recommendations and to enable a business solution or range of solutions to be achieved
Communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
Manage expectations and present user research insight, proposed solutions and/or test findings to clients and stakeholders.
Provide direction and technical guidance for the business with regard to AI and data science opportunities
Work autonomously and interact effectively within wide, multidisciplinary teams
Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers with conflicting priorities, interests and timescales
Manipulate, analyse and visualise complex datasets
Select datasets and methodologies most appropriate to the business problem
Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes
Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
Identify appropriate resources and architectures for solving a computational problem within the workplace
Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
Develop, build and maintain the services and platforms that deliver AI and data science
Define requirements for, and supervise implementation of, and use data management infrastructure, including enterprise, private and public cloud resources and services
Consistently implement data curation and data quality controls
Develop tools that visualise data systems and structures for monitoring and performance
Use scalable infrastructures, high performance networks, infrastructure and services management and operation to generate effective business solutions.
Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets
Identify and quantify different kinds of uncertainty in the outputs of data collection, experiments and analyses
Apply scientific methods in a systematic process through experimental design, exploratory data analysis and hypothesis testing to facilitate business decision making
Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice
Apply research methodology and project management techniques appropriate to the organisation and products
Select and use programming languages and tools, and follow appropriate software development practices
Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems
Analyse information, frame questions and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements
Undertakes independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances

Behaviours 8

A strong work ethic and commitment in order to meet the standards required.
Reliable, objective and capable of independent and team working
Acts with integrity with respect to ethical, legal and regulatory ensuring the protection of personal data, safety and security
Initiative and personal responsibility to overcome challenges and take ownership for business solutions
Commitment to continuous professional development; maintaining their knowledge and skills in relation to AI developments that influence their work
Is comfortable and confident interacting with people from technical and non-technical backgrounds. Presents data and conclusions in a truthful and appropriate manner
Participates and shares best practice in their organisation, and the wider community around all aspects of AI data science
Maintains awareness of trends and innovations in the subject area, utilising a range of academic literature, online sources, community interaction, conference attendance and other methods which can deliver business value

Live vacancies

No live vacancies for this course as of 2026-07-24 — new adverts appear on Find an apprenticeship daily, and many degree apprenticeship employers recruit on their own careers sites instead.

Universities registered to deliver it

3 universities are on the government provider register for this standard. Registration means they can deliver it — live vacancies depend on employer partnerships each recruitment cycle.

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Official sources

Occupational summary, duties, knowledge, skills and behaviours reproduced from the ST0763 apprenticeship standard (last updated 2021-04-30) — contains public sector information licensed under the Open Government Licence v3.0. Always check the official page before applying: standards get revised.