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Apprenticeship training course

Artificial intelligence (AI) data specialist (level 7)

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Information about Artificial intelligence (AI) data specialist (level 7)

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

Knowledge, skills and behaviours
View knowledge, skills and behaviours

Knowledge

  • 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

  • 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

  • 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
Apprenticeship category (sector)
Digital
Qualification level
7
Equal to master’s degree
Course duration
24 months
Maximum funding
£17,000
Maximum government funding for
apprenticeship training and assessment costs.
Job titles include
  • Machine learning engineer
  • Artificial intelligence engineer
  • Director AI
  • AI strategy manager
  • Artificial intelligence specialist
  • Machine learning specialist

View more information about Artificial intelligence (AI) data specialist (level 7) from the Institute for Apprenticeships and Technical Education.