Mid Green occupation

Digital

Artificial intelligence AI data specialist

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

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

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

Technical Educational Products

: ST0763: Artificial intelligence (AI) data specialist (Level 7) Approved for delivery

Reference:: OCC0763

Status:: Approved occupation

Average (median) salary:: £49,873 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
- 2134/03 Software developers
- 3544/00 Data analysts

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

Technical Educational Products

: ST0763: Artificial intelligence (AI) data specialist (Level 7) Approved for delivery

Reference:: OCC0763

Status:: Approved occupation

Average (median) salary:: £49,873 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
- 2134/03 Software developers
- 3544/00 Data analysts

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

Technical Educational Products

: ST0763: Artificial intelligence (AI) data specialist (Level 7) Approved for delivery

Reference:: OCC0763

Status:: Approved occupation

Average (median) salary:: £49,873 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
- 2134/03 Software developers
- 3544/00 Data analysts