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Artificial Intelligence AI Data Specialist

Artificial Intelligence AI Data Specialist


Level 7 - Professional Occupation

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

Reference: OCC0763

Status: assignment_turned_inApproved occupation

Average (median) salary: £43,283 per year

SOC 2020 code: 2433 Actuaries, economists and statisticians

SOC 2020 sub unit groups:

  • 2433/04 Statistical data scientists
  • 2133/01 Computer analysts and scientists
  • 2133/03 Data engineers
  • 2133/04 IT systems architects
  • 3544/00 Data analysts

Technical Education Products


Artificial intelligence (AI) data specialist

(Level 7)

Approved for delivery

Employers involved in creating the standard:

British Broadcasting Corporation, Public Health England, Bank of England, Royal Mail Group, Unilever, TUI, Aviva, Shop Direct, Defence Science Technology Laboratory – MOD, Ericsson, First Response Finance LTD, GlaxoSmithKline, AstraZeneca, EasyJet, BT, Barclays, Machinable, Office of National Statistics, UBS


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.

Employers involved in creating the standard:

British Broadcasting Corporation, Public Health England, Bank of England, Royal Mail Group, Unilever, TUI, Aviva, Shop Direct, Defence Science Technology Laboratory – MOD, Ericsson, First Response Finance LTD, GlaxoSmithKline, AstraZeneca, EasyJet, BT, Barclays, Machinable, Office of National Statistics, UBS

Typical job titles include:

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



Knowledge, skills and behaviours (KSBs)

K1: 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
K2: 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
K3: How to apply advanced statistical and mathematical methods to commercial projects
K4: How to extract data from systems and link data from multiple systems to meet business objectives
K5: How to design and deploy effective techniques of data analysis and research to meet the needs of the business and customers
K6: 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
K7: How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance and usability testing
K8: How to interpret organisational policies, standards and guidelines in relation to AI and data
K9: 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.
K10: How own role fits with, and supports, organisational strategy and objectives
K11: The roles and impact of AI, data science and data engineering in industry and society
K12: 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
K13: How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace
K14: The business value of a data product that can deliver the solution in line with business needs, quality standards and timescales
K15: The engineering principles used (general and software) to investigate and manage the design, development and deployment of new data products within the business
K16: Understand high-performance computer architectures and how to make effective use of these
K17: How to identify current industry trends across AI and data science and how to apply these
K18: The programming languages and techniques applicable to data engineering
K19: The principles and properties behind statistical and machine learning methods
K20: How to collect, store, analyse and visualise data
K21: How AI and data science techniques support and enhance the work of other members of the team
K22: The relationship between mathematical principles and core techniques in AI and data science within the organisational context
K23: The use of different performance and accuracy metrics for model validation in AI projects
K24: Sources of error and bias, including how they may be affected by choice of dataset and methodologies applied
K25: Programming languages and modern machine learning libraries for commercially beneficial scientific analysis and simulation
K26: The scientific method and its application in research and business contexts, including experiment design and hypothesis testing
K27: The engineering principles used (general and software) to create new instruments and applications for data collection
K28: How to communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
K29: The need for accessibility for all users and diversity of user needs

S1: 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
S2: Independently analyse test data, interpret results and evaluate the suitability of proposed solutions, considering current and future business requirements
S3: 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
S4: Communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
S5: Manage expectations and present user research insight, proposed solutions and/or test findings to clients and stakeholders.
S6: Provide direction and technical guidance for the business with regard to AI and data science opportunities
S7: Work autonomously and interact effectively within wide, multidisciplinary teams
S8: Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers with conflicting priorities, interests and timescales
S9: Manipulate, analyse and visualise complex datasets
S10: Select datasets and methodologies most appropriate to the business problem
S11: Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes
S12: Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
S13: Identify appropriate resources and architectures for solving a computational problem within the workplace
S14: Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
S15: Develop, build and maintain the services and platforms that deliver AI and data science
S16: Define requirements for, and supervise implementation of, and use data management infrastructure, including enterprise, private and public cloud resources and services
S17: Consistently implement data curation and data quality controls
S18: Develop tools that visualise data systems and structures for monitoring and performance
S19: Use scalable infrastructures, high performance networks, infrastructure and services management and operation to generate effective business solutions.
S20: Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets
S21: Identify and quantify different kinds of uncertainty in the outputs of data collection, experiments and analyses
S22: Apply scientific methods in a systematic process through experimental design, exploratory data analysis and hypothesis testing to facilitate business decision making
S23: Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice
S24: Apply research methodology and project management techniques appropriate to the organisation and products
S25: Select and use programming languages and tools, and follow appropriate software development practices
S26: Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems
S27: Analyse information, frame questions and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements
S28: Undertakes independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances

B1: A strong work ethic and commitment in order to meet the standards required.
B2: Reliable, objective and capable of independent and team working
B3: Acts with integrity with respect to ethical, legal and regulatory ensuring the protection of personal data, safety and security
B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions
B5: Commitment to continuous professional development; maintaining their knowledge and skills in relation to AI developments that influence their work
B6: Is comfortable and confident interacting with people from technical and non-technical backgrounds. Presents data and conclusions in a truthful and appropriate manner
B7: Participates and shares best practice in their organisation, and the wider community around all aspects of AI data science
B8: 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


Duty D1

Initiate new projects in an agile environment, and collaboratively maintain technical standards within AI solutions applied across the organisation and its customers.

Duty D2

Critically evaluate and synthesise research findings in AI and related fields and translate into organisational context.

Duty D3

Use the conclusions drawn from applied research in order to develop innovative, scalable data-driven AI solutions for business problems

Duty D4

Contribute to the development and ethical and legal conduct of AI systems and processes, in line with organisational and regulatory requirements.

Duty D5

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.

Duty D6

Develop innovative approaches to tackle known business problems that previously did not have a feasible solution within the constraints of a specific business context.

Duty D7

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.

Duty D8

Enhance awareness of the wider application of AI tools and technologies across the business so that opportunities for its use can be identified

Duty D9

Develop and architect new robust data sourcing and processing systems to serve the organisation.

Duty D10

Design technical roadmaps for data life-cycles ensuring appropriate support and business processes are in place.

Duty D11

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

Duty D12

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.

Duty D13

Assess risks/limitations and quantify biases associated with applications of AI within given business contexts.

Duty D14

Provide technical authority for the business regarding emerging opportunities for AI.

Duty D15

Practice continuous self-learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development

Occupational Progression

This occupational progression map shows technical occupations that have transferable knowledge and skills.

In this map, the focused occupation is highlighted in yellow. The arrows indicate where transferable knowledge and skills exist between two occupations. This map shows some of the strongest progression links between the focused occupation and other occupations.

It is anticipated that individuals would be required to undertake further learning or training to progress to and from occupations. To find out more about an occupation featured in the progression map, including the learning options available, click the occupation.

Progression decisions have been reached by comparing the knowledge and skills statements between occupational standards, combined with individualised learner movement data.

Technical Occupations

Levels 2-3

Higher Technical Occupations

Levels 4-5

Professional Occupations

Levels 6-7

Progression link into focused occupation.

Level 4

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Level 6

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Level 6

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Level 7