ST0763Artificial Intelligence (AI) Data Specialist Standards

National Competency Framework for professionals using data to improve and automate business processes.

AI DATA SPECIALISTOccupation summary

Overview of the role: A professional using data to improve and automate business processes.

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.

AI Data Specialist’s responsibilities include:

  • Developing AI Solutions: Create and implement AI tools to automate business processes and support decision-making through applied research.
  • Handling Complex Data: Manage large, complex datasets that exceed traditional analysis methods, using advanced computational techniques.
  • Promoting AI Adoption: Champion new AI tools and technologies within the organization, aligning with data governance and ethical practices.
  • Delivering Value through AI: Enhance products and processes using machine learning to improve data quality and business value.
  • Stakeholder Communication: Engage and collaborate with leaders, data teams, and external partners.
  • Collaboration and Leadership: Lead AI projects and identify new AI-driven business opportunities.
  • Project Management: Initiate agile projects, uphold technical standards, and work closely with policy and operations teams.

Typical job titles include:

  • AI Strategy Manager
  • Artificial Intelligence Engineer
  • Artificial Intelligence Specialist
  • Director of AI
  • Machine Learning Engineer
  • Machine Learning Specialist

MAPPEDAI DATA SPECIALIST STANDARD

Standard No. ST0763. This standard is from and belongs to IfATE and is used under the terms of the Open Government Licence in the United Kingdom
LEVEL EQF7
ROUTEInformation Technology
EQAOfqual UK.Gov
APPLIEDMaster of Artificial Intelligence
STATUSApproved for Applied

AI DATA SPECIALISTOccupation duties & KSBs

Duty 1

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

Knowledge must have for Duty 1:

  • 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.
  • 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.
  • K13: How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace.
  • K17: How to identify current industry trends across AI and data science and how to apply these.
  • K21: How AI and data science techniques support and enhance the work of other members of the team.
  • K29: The need for accessibility for all users and diversity of user needs.

Skill must have for Duty 1:

  • 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.
  • 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.
  • K13: How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace.
  • K17: How to identify current industry trends across AI and data science and how to apply these.
  • K21: How AI and data science techniques support and enhance the work of other members of the team.
  • K29: The need for accessibility for all users and diversity of user needs.

Behaviour must have for Duty 1:

  • B2: Reliable, objective, and capable of independent and team working.
  • B5: Commitment to continuous professional development; maintaining their knowledge and skills in relation to AI developments that influence their work.
  • 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 2

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

Knowledge must have for Duty 2:

  • 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.
  • K3: How to apply advanced statistical and mathematical methods to commercial projects.
  • K7: How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance, and usability testing.
  • 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.
  • 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.
  • K26: The scientific method and its application in research and business contexts, including experiment design and hypothesis testing.

Skills must have for Duty 2:

  • 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.
  • 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.
  • S24: Apply research methodology and project management techniques appropriate to the organisation and products.
  • S26: Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems.

Behavior must have for Duty 2:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
  • 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 3

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

 

Knowledge must have for Duty 3:

  • 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.
  • K5: How to design and deploy effective techniques of data analysis and research to meet the needs of the business and customers.
  • K7: How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance, and usability testing.
  • K14: The business value of a data product that can deliver the solution in line with business needs, quality standards, and timescales.
  • 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.
  • K26: The scientific method and its application in research and business contexts, including experiment design and hypothesis testing.
  • K28: How to communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly.

Skills must have for Duty 3:

  • 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.
  • S5: Manage expectations and present user research insight, proposed solutions, and/or test findings to clients and stakeholders.
  • S9: Manipulate, analyse, and visualise complex datasets.
  • 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.
  • 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.

Behavior must have for Duty 3:

  • B2: Reliable, objective, and capable of independent and team working.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
  • B7: Participates and shares best practice in their organisation and the wider community around all aspects of AI data science.
Duty 4

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

Knowledge must have for Duty 4:

  • 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.
  • K24: Sources of error and bias, including how they may be affected by choice of dataset and methodologies applied.
  • K29: The need for accessibility for all users and diversity of user needs.

Skills must have for Duty 4:

  • S6: Provide direction and technical guidance for the business with regard to AI and data science opportunities.
  • S8: Coordinate, negotiate with and manage expectations of diverse stakeholders/suppliers with conflicting priorities, interests, and timescales.
  • S12: Consider the associated regulatory, legal, ethical, and governance issues when evaluating choices at each stage of the data process.
  • S17: Consistently implement data curation and data quality controls.

Behavior must have for Duty 4:

  • 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.
  • B7: Participates and shares best practice in their organisation, and the wider community around all aspects of AI data science.
Duty 5

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.

Knowledge must have for Duty 5:

  • 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.
  • K13: How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace.
  • 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.
  • K19: The principles and properties behind statistical and machine learning methods.
  • K26: The scientific method and its application in research and business contexts, including experiment design and hypothesis testing.
  • K29: The need for accessibility for all users and diversity of user needs.

Skills must have for Duty 5:

  • 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 the implementation of, and use data management infrastructure, including enterprise, private, and public cloud resources and services.
  • S19: Use scalable infrastructures, high-performance networks, infrastructure, and services management and operation to generate effective business solutions.
  • S25: Select and use programming languages and tools, and follow appropriate software development practices.

Behavior must have for Duty 5:

  • B3: Acts with integrity with respect to ethical, legal, and regulatory ensuring the protection of personal data, safety, and security.
  • B7: Participates and shares best practice in their organisation and the wider community around all aspects of AI data science.
Duty 6

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

Knowledge must have for Duty 6:

  • 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.
  • K7: How to solve problems and evaluate software solutions via analysis of test data and results from research, feasibility, acceptance, and usability testing.
  • K13: How to identify the compromises and trade-offs which must be made when translating theory into practice in the workplace.
  • K29: The need for accessibility for all users and diversity of user needs.

Skills must have for Duty 6:

  • 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.
  • S6: Provide direction and technical guidance for the business with regard to AI and data science opportunities.
  • S13: Identify appropriate resources and architectures for solving a computational problem within the workplace.
  • S27: Analyse information, frame questions, and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements.

Behavior must have for Duty 6:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • 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.
  • B6: Is comfortable and confident interacting with people from technical and non-technical backgrounds. Presents data and conclusions in a truthful and appropriate manner.
Duty 7

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.

Knowledge must have for Duty 7:

  • K5: How to design and deploy effective techniques of data analysis and research to meet the needs of the business and customers.
  • 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.
  • K20: How to collect, store, analyse, and visualise data.
  • K23: The use of different performance and accuracy metrics for model validation in AI projects.
  • K25: Programming languages and modern machine learning libraries for commercially beneficial scientific analysis and simulation.
  • K27: The engineering principles used (general and software) to create new instruments and applications for data collection.

Skills must have for Duty 7:

  • S14: Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
  • 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.
  • 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.
  • S28: Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable, and changing circumstances.

Behavior must have for Duty 7:

  • B2: Reliable, objective, and capable of independent and team working.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
  • B7: Participates and shares best practice in their organisation, and the wider community around all aspects of AI data science.
Duty 8

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

Knowledge must have for Duty 8:

  • K10: How to interpret organisational policies, standards, and guidelines in relation to AI and data.
  • 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.
  • K14: The business value of a data product that can deliver the solution in line with business needs, quality standards, and timescales.
  • K21: How AI and data science techniques support and enhance the work of other members of the team.
  • K24: Sources of error and bias, including how they may be affected by choice of dataset and methodologies applied.
  • 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.

Skills must have for Duty 8:

  • S4: Communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly.
  • 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.
  • S11: Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes.
  • S23: Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice.
  • S27: Analyse information, frame questions, and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements.

Behavior must have for Duty 8:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • B5: Commitment to continuous professional development; maintaining their knowledge and skills in relation to AI developments that influence their work.
  • 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 9

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

Knowledge must have for Duty 9:

  • 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.
  • K4: How to extract data from systems and link data from multiple systems to meet business objectives.
  • 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.
  • 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.
  • K18: The programming languages and techniques applicable to data engineering.

Skills must have for Duty 9:

  • 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.
  • 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.
  • S10: Select datasets and methodologies most appropriate to the business problem.
  • S13: Identify appropriate resources and architectures for solving a computational problem within the workplace.
  • S25: Select and use programming languages and tools, and follow appropriate software development practices.

Behavior must have for Duty 9:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • 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.
  • B6: Is comfortable and confident interacting with people from technical and non-technical backgrounds. Presents data and conclusions in a truthful and appropriate manner.
Duty 10

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

Knowledge must have for Duty 10:

  • 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.
  • 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.

Skills must have for Duty 10:

  • 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.
  • S17: Consistently implement data curation and data quality controls.
  • S18: Develop tools that visualise data systems and structures for monitoring and performance.
  • S25: Select and use programming languages and tools, and follow appropriate software development practices.

Behavior must have for Duty 10:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • B2: Reliable, objective, and capable of independent and team working.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
  • B7: Participates and shares best practice in their organisation and the wider community around all aspects of AI data science.
Duty 11

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.

Knowledge must have for Duty 11:

  • K16: Understand high-performance computer architectures and how to make effective use of 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.
  • K22: The relationship between mathematical principles and core techniques in AI and data science within the organisational context.

Skills must have for Duty 11:

  • 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.
  • S20: Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets.
  • S25: Select and use programming languages and tools, and follow appropriate software development practices.

Behavior must have for Duty 11:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • B2: Reliable, objective, and capable of independent and team working.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
Duty 12

Identify best practices in AI data systems, data structures, data architecture, and data warehousing technologies and provide technical oversight in order to meet business objectives.

Knowledge must have for Duty 12:

  • 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.
  • K4: How to extract data from systems and link data from multiple systems to meet business objectives.
  • 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.
  • 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.
  • K24: Sources of error and bias, including how they may be affected by choice of dataset and methodologies applied.

Skills must have for Duty 12:

  • 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.
  • 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.
  • S13: Identify appropriate resources and architectures for solving a computational problem within the workplace.
  • S23: Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice.

Behavior must have for Duty 12:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • 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.
Duty 13

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

Knowledge must have for Duty 13:

  • 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.
  • K3: How to apply advanced statistical and mathematical methods to commercial projects.
  • K22: The relationship between mathematical principles and core techniques in AI and data science within the organisational context.
  • 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.

Skills must have for Duty 13:

  • 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.
  • 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.
  • S28: Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.

Behavior must have for Duty 13:

  • B1: A strong work ethic and commitment in order to meet the standards required.
  • B4: Initiative and personal responsibility to overcome challenges and take ownership for business solutions.
  • 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 14

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

Knowledge must have for Duty 12:

  • K8: How to interpret organisational policies, standards, and guidelines in relation to AI and data.
  • 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.
  • K17: How to identify current industry trends across AI and data science and how to apply these.
  • K21: How AI and data science techniques support and enhance the work of other members of the team.
  • K28: How to communicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly.

Skills must have for Duty 12:

  • 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.
  • 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.
  • S23: Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice.

Behavior must have for Duty 12:

  • 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 15

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

Knowledge must have for Duty 15:

  • K10: How own role fits with, and supports, organisational strategy and objectives.
  • K17: How to identify current industry trends across AI and data science and how to apply these.

Skills must have for Duty 15:

  • S7: Work autonomously and interact effectively within wide, multidisciplinary teams.
  • S11: Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes.

Behavior must have for Duty 15:

  • B5: Commitment to continuous professional development; maintaining their knowledge and skills in relation to AI developments that influence their work.
  • 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.

APPLIEDPrograms mapped with
AI Data Specialist Standard

Off-campus

FEDERAL STATUSBecome Swiss University 2027

https://qaqc.ch/wp-content/uploads/2024/07/SIMI-QAQC-Logo-4-160x160.png

SIMI Swiss is actively preparing to seek accreditation from the Swiss Agency of Accreditation and Quality Assurance (AAQ), which is approved by the Swiss Accreditation Council (SAC), by 2027.

https://qaqc.ch/wp-content/uploads/2024/11/Untitled-design-36.png

FEDERAL STATUSBecome Swiss University 2027

SIMI Swiss is actively preparing to seek accreditation from the Swiss Agency of Accreditation and Quality Assurance (AAQ), which is approved by the Swiss Accreditation Council (SAC), by 2027.

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