ARTIFICIAL INTELLIGENCE (AI) DATA SPECIALIST (L7)
Artificial Intelligence (AI) Data Specialist (Level 7)
Discover new artificial intelligence solutions that use data to improve and automate business processes.
SOC 2020 code
Level 7 RQF
Details of standard
Artificial Intelligence (AI) Data Specialist
Summary
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.
Typical job titles include
AI Strategy Manager
Responsible for shaping the organisation’s overall direction in artificial intelligence. They analyse market trends, identify opportunities for AI integration, and ensure alignment between technology initiatives and business goals.
Artificial Intelligence Engineer
Designs, develops, and deploys AI systems and algorithms. They work with large datasets, build intelligent models, and integrate AI capabilities into applications to solve real-world problems.
Artificial Intelligence Specialist
Focuses on applying AI techniques to address specific business or technical issues. They support AI solution development, model optimisation, and system implementation to enhance performance and efficiency.
Director of AI
Leads the enterprise-wide AI strategy and innovation roadmap. They manage teams of specialists, oversee project portfolios, and ensure responsible and effective AI adoption at the senior leadership level.
Machine Learning Engineer
Builds machine learning pipelines, trains predictive models, and ensures their scalability and reliability in production environments. They combine advanced programming skills with strong data science capabilities.
Machine Learning Specialist
Concentrates on designing, testing, and improving machine learning algorithms. They work closely with data experts to deliver models that support automation, decision-making, and product enhancement.
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
Knowledge
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.
Skills
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 and 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, 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: Undertake independent, impartial decision-making, respecting the opinions and views of others in complex, unpredictable and changing circumstances.
Behaviours
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 requirements, ensuring the protection of personal data, safety and security.
B4: Shows initiative and personal responsibility to overcome challenges and take ownership for business solutions.
B5: Committed to continuous professional development; maintaining knowledge and skills related to AI advancements that influence their work.
B6: Comfortable and confident when interacting with people from both technical and non-technical backgrounds; presents data and conclusions truthfully and appropriately.
B7: Participates in and shares best practice within their organisation and the wider community in all aspects of AI and data science.
B8: Maintains awareness of trends and innovations in the field, utilising academic literature, online sources, community interaction, conferences and other methods that deliver business value.
Duties
D1: Initiate new projects in an agile environment and collaboratively maintain technical standards within AI solutions applied across the organisation and its customers.
D2: Critically evaluate and synthesise research findings in AI and related fields and translate them into the organisational context.
D3: Use conclusions drawn from applied research to develop innovative, scalable, data-driven AI solutions for business problems.
D4: Contribute to the ethical and legal development and operation of AI systems and processes, in line with organisational and regulatory requirements.
D5: Investigate and design efficient, effective architectures to enable and maximise the use and business impact of AI systems and solutions.
D6: Develop innovative approaches to tackle established business problems that previously lacked feasible solutions within operational constraints.
D7: Initiate and design scalable batch and real-time analytical solutions to business challenges using AI, data science, machine learning, statistics and related technologies.
D8: Promote awareness and understanding of AI tools and technologies across the business to identify new opportunities for applied use.
D9: Develop and architect robust data sourcing and processing systems to support organisational requirements.
D10: Design technical roadmaps for data lifecycles, ensuring alignment with business support structures and operational processes.
D11: Create and optimise mechanisms for accessing and analysing large, complex, diverse or high-velocity datasets where traditional approaches are unsuitable, to deliver business value.
D12: Identify best practices in AI data systems, data structures, data architectures and data warehousing, providing technical oversight to meet business objectives.
D13: Assess risks, limitations and quantify biases associated with AI applications within specific business contexts.
D14: Provide specialist technical authority to the business regarding emerging AI opportunities.
D15: Engage in continuous self-learning to stay current with technological developments, enhancing professional skills and taking responsibility for ongoing professional development.