Interested in a simple Skills Scan and Learning Progress Tracker tool for the Artificial intelligence (AI) data specialist standard?
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 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
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 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 1
DUTY: Initiate new projects in an agile environment, and collaboratively maintain technical standards within AI solutions applied across the organisation and its customers.
CRITERIA FOR MEASURING PERFORMANCE: Research prototypes are developed to organisational/customer requirements in line with industry standards.
K1
K5
K6
K13
K17
K21
K29
S4
S5
S11
S12
S21
S22
S23
S24
S28
B2
B5
B8
Duty 2
DUTY: Critically evaluate and synthesise research findings in AI and related fields and translate into organisational context.
CRITERIA FOR MEASURING PERFORMANCE: Research findings in AI and related fields are clearly articulated and documented, translating them into potential impacts, opportunities and threats for the organisation.
K1
K3
K7
K17
K18
K19
K21
K22
K26
S2
S3
S4
S5
S6
S11
S12
S24
S26
B1
B4
B8
Duty 3
DUTY: Use the conclusions drawn from applied research in order to develop innovative, scalable data-driven AI solutions for business problems
CRITERIA FOR MEASURING PERFORMANCE: New projects are initiated and maintained to organisational/customer requirements in line with industry standards.
K2
K5
K7
K14
K17
K18
K19
K26
K28
S2
S3
S5
S9
S11
S12
S24
S25
S26
B2
B4
B7
Duty 4
DUTY: Contribute to the development and ethical and legal conduct of AI systems and processes, in line with organisational and regulatory requirements.
CRITERIA FOR MEASURING PERFORMANCE: Effective solutions are delivered in accordance with principles of responsible research and innovation for automated AI decision-making systems. Governance frameworks are established which take into account legal and regulatory requirements including privacy issues.
K8
K9
K10
K11
K12
K24
K29
S6
S8
S12
S17
B1
B2
B3
B7
Duty 5
DUTY: 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.
CRITERIA FOR MEASURING PERFORMANCE: Effective architectures are delivered in line with agreed timescales and to organisational requirements.
K2
K13
K15
K16
K19
K26
K29
S13
S14
S15
S16
S19
S25
B3
B7
Duty 6
DUTY: Develop innovative approaches to tackle known business problems that previously did not have a feasible solution within the constraints of a specific business context.
CRITERIA FOR MEASURING PERFORMANCE: Innovative approaches are developed to meet industry standards utilising a full range of AI and related technologies to create and build solutions that can be used by strategic or operational users and can be further integrated into business systems.
K1
K7
K13
K29
S3
S6
S13
S27
B1
B3
B4
B6
Duty 7
DUTY: 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.
CRITERIA FOR MEASURING PERFORMANCE: Solutions are designed and developed in line with agreed timescales and organisational and industry standards.
K5
K17
K18
K20
K23
K25
K27
S14
S18
S19
S20
S25
S26
S28
B2
B4
B7
Duty 8
DUTY: Enhance awareness of the wider application of AI tools and technologies across the business so that opportunities for its use can be identified
CRITERIA FOR MEASURING PERFORMANCE: The use of AI and its applications are championed within the organisation and novel tools and technologies are adopted.
K10
K11
K12
K14
K21
K24
K27
K28
S4
S7
S8
S11
S23
S27
B1
B5
B7
B8
Duty 9
DUTY: Develop and architect new robust data sourcing and processing systems to serve the organisation.
CRITERIA FOR MEASURING PERFORMANCE: New data sources are integrated into business processes in line with organisational change management processes. Analytics and statistical methods for data preparation and pre-processing are applied. Opportunities are identified to integrate data from silos both within and outside the organisation, to provide value added insights. These data pipelines should follow organisational and general architecture best practice.
K2
K4
K7
K8
K15
K16
K18
S1
S2
S4
S5
S6
S10
S13
S14
S15
S16
S25
B1
B3
B4
Duty 10
DUTY: Design technical roadmaps for data life-cycles ensuring appropriate support and business processes are in place.
CRITERIA FOR MEASURING PERFORMANCE: Technical roadmaps are designed and maintained to organisational requirements. Clear plans for evolution of the technologies, and the relevant support and business processes, are in place.
K6
K9
K10
S1
S17
S18
S25
B1
B2
B4
B7
Duty 11
DUTY: 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
CRITERIA FOR MEASURING PERFORMANCE: Bespoke problem-specific mechanisms that consider performance limitations are developed and tested to meet organisational/customer data access and analysis requirements.
K16
K18
K19
K20
K22
K23
S8
S9
S10
S20
S25
S26
B1
B2
B4
Duty 12
DUTY: 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.
CRITERIA FOR MEASURING PERFORMANCE: Future business/domain opportunities are researched, identified and completed in line with organisational/customer requirements. Rigorous scientific methodology is followed at all stages of research activity, including communication of uncertainty in results of experiments and analysis.
K2
K4
K7
K8
K15
K16
K24
S1
S2
S4
S5
S6
S10
S13
S23
B1
B6
B7
Duty 13
DUTY: Assess risks/limitations and quantify biases associated with applications of AI within given business contexts.
CRITERIA FOR MEASURING PERFORMANCE: Risks are assessed according to organisational policy/customer requirements/industry standards.
K1
K3
K22
K24
K25
S10
S11
S12
S13
S21
S22
S28
B1
B4
B8
Duty 14
DUTY: Provide technical authority for the business regarding emerging opportunities for AI.
CRITERIA FOR MEASURING PERFORMANCE: Direction and guidance for the business is clearly articulated to industry standards. Strategic opportunities are identified and new insights relevant to business goals are generated.
K8
K10
K11
K12
K17
K21
K28
S4
S5
S6
S7
S18
S19
S23
S26
B6
B7
B8
Duty 15
DUTY: Practice continuous self-learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development
