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Data scientist

Find out what a data scientist in government does and the skills you need to do the role at each level.

Last updated 30 August 2022 — See all updates

What a data scientist does

Data science is a broad and fast-moving field spanning maths, statistics, software engineering and communications. Data scientists will often work as part of a multidisciplinary team, using data and analytics to inform and achieve organisational goals.

In this role, you will:

  • be inquisitive
  • explore and visualise data
  • make recommendations to address complex problems and to inform strategic and operational decision making
  • use data ethically and appropriately
  • be innovative and adaptable
  • explore existing and new data using a range of statistical tools and techniques, such as machine learning and predictive analytics
  • find patterns in data and transform them into organisational insight

Data scientist role levels

There are 6 data scientist role levels, from trainee data scientist to head of data science.

The typical responsibilities and skills for each role level are described in the sections below. You can use this to identify the skills you need to progress in your career, or simply to learn more about each role in the Government Digital and Data profession.

1. Trainee data scientist

A trainee data scientist is inquisitive, curious about data and keen to learn and develop.

At this role level, you will:

  • have a basic understanding of software development and analytical methods
  • interpret statistical outputs effectively
  • be aware of how data science techniques can be used, and have a basic knowledge of those key techniques, such as machine learning
  • work as part of a multidisciplinary team to develop data science solutions and outputs
  • prepare and manipulate data
  • present effectively
  • be aware of ethical considerations
  • be aware of the technologies used

This role level is often performed at the Civil Service job grade of:

  • AO (Administrative Officer)
  • EO (Executive Officer)
Skill Description

Applied maths, statistics and scientific practices

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show an understanding of the benefits of applied mathematics and statistics, and can use this knowledge to carry out data science tasks
  • carry out general analysis techniques for data inspection, exploration and visualisation
  • interpret statistical output effectively and accurately
  • show an awareness of different performance and accuracy metrics for statistical assessment and validation

Data engineering and manipulation

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show an awareness of the need for engineering support to design and deliver products into the organisation
  • appreciate the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place
  • show an awareness of different architectures (including cloud and on-premise) and data manipulation and transformation tools

Data science innovation

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • adopt an inquisitive and curious approach to data
  • be receptive to learning about data science and its techniques
  • be unafraid to ask questions and discover what there is to learn

Delivering business impact

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show an understanding of the organisation and the benefits of data science
  • develop data science products as part of a team
  • create basic visuals and presentations to communicate data science effectively
  • show knowledge of data and analysis and can discuss how to align them to meet user needs

Developing data science capability

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show an awareness of various sources of training and self-directed learning for data science
  • create a CPD (continuous professional development) plan with support from your manager
  • demonstrate a basic understanding of key data science techniques, such as machine learning, and how they can be used

Ethics and privacy (data science)

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show that you know the appropriate channels to discuss ethical issues

Programming and build (data science)

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • show a basic understanding of software development principles and can write simple scripts under supervision

Understanding product delivery

Level: awareness

Awareness is the first of 4 ascending skill levels

You can:

  • manage your contribution to tasks to fit in with the work of your wider team

2. Associate data scientist

An associate data scientist is inquisitive, curious about data and keen to learn and develop.

At this role level, you will:

  • understand the basics of software development and analytical methods, and be able to develop basic models
  • understand and be able to implement a range of data science techniques, including machine learning
  • collaborate with others to develop data science solutions and outputs supporting the organisation
  • prepare and manipulate data, and perform analytics
  • present and communicate effectively
  • be aware of project delivery methods
  • be aware of ethical considerations
  • have knowledge of the technologies used

This role level is often performed at the Civil Service job grade of:

  • EO (Executive Officer)
  • HEO (Higher Executive Officer)
Skill Description

Applied maths, statistics and scientific practices

Level: working

Working is the second of 4 ascending skill levels

You can:

  • apply analytical methods including exploratory data analysis and statistical testing to a specific data set, to reach accurate and reliable conclusions
  • understand and use different performance and accuracy metrics for model validation in data science projects, hypothesis testing and information retrieval
  • compare selected applied mathematics and statistical methods and identify their differences
  • access and use the statistical and scientific tools available within the organisation

Data engineering and manipulation

Level: working

Working is the second of 4 ascending skill levels

You can:

  • identify and engage with the appropriate engineering support to design and deliver products into the organisation
  • understand the reasons for cleansing and preparing data before including it in data science products, recognise the processes and tools involved, and put reusable processes and checks in place
  • access and use different architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation

Data science innovation

Level: working

Working is the second of 4 ascending skill levels

You can:

  • adopt an inquisitive and curious approach to data
  • seek out and research new data science techniques to support learning
  • ask questions to improve your knowledge and learn about data science norms
  • see possibilities for improvements and innovation

Delivering business impact

Level: working

Working is the second of 4 ascending skill levels

You can:

  • show an understanding of the organisation and the benefits of data science
  • collaborate to help identify user needs and develop and deliver data science products
  • communicate effectively and present analysis and visualisations tailored to your audience

Developing data science capability

Level: working

Working is the second of 4 ascending skill levels

You can:

  • manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
  • confidently talk about the benefits of data science approaches to existing and potential customers
  • demonstrate a good understanding of key data science techniques, such as machine learning, and you can use them to build data science solutions, including reports, models and dashboards

Ethics and privacy (data science)

Level: working

Working is the second of 4 ascending skill levels

You can:

  • show an understanding of the ethical implications of using data science in your projects
  • identify the person within the organisation to raise concerns or suggestions with regarding ethical considerations and compliance

Programming and build (data science)

Level: working

Working is the second of 4 ascending skill levels

You can:

  • write and test scripts and create basic models in one or more languages
  • collaborate on shared codebases, using a variety of methodologies

Understanding product delivery

Level: working

Working is the second of 4 ascending skill levels

You can:

  • show an awareness of the differences between delivery methods, such as Agile and waterfall, and can propose the most appropriate method to deliver each product
  • manage your contribution to tasks to fit in with the work of your wider team

3. Data scientist

A data scientist will work independently and have a good understanding of a range of topics, including data science techniques, delivery methods and stages (such as minimal viable products), tools and technologies.

At this role level, you will:

  • develop complex solutions using a range of data science techniques, whilst understanding any ethical considerations
  • understand the role and benefits of data science within the organisation
  • support capability building within the organisation
  • collaborate with others to develop data science solutions and outputs supporting the organisation
  • prepare and manipulate data, and perform complex analytics
  • present and communicate effectively

This role level is often performed at the Civil Service job grade of:

  • HEO (Higher Executive Officer)
  • SEO (Senior Executive Officer)
Skill Description

Applied maths, statistics and scientific practices

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • apply designated quantitative techniques such as time series analysis, optimisation and simulation to create and embed appropriate models for analysis and prediction
  • provide guidance on matching data sources with relevant applied mathematics and statistical techniques to meet analysis goals
  • apply appropriate statistical techniques to available data to discover new relations and offer insight into research problems, helping to improve organisational processes and support decision making
  • access and use the statistical tools available within the organisation

Data engineering and manipulation

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • work with data engineers and data scientists to design and deliver products into the organisation effectively
  • understand the reasons for cleansing and preparing data before including it in data science products and can put reusable processes and checks in place
  • access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation

Data science innovation

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • demonstrate practical knowledge of data science tools and techniques
  • develop data science solutions that maximise insight
  • identify opportunities for how data science can improve data practices

Delivering business impact

Level: working

Working is the second of 4 ascending skill levels

You can:

  • show an understanding of the organisation and the benefits of data science
  • collaborate to help identify user needs and develop and deliver data science products
  • communicate effectively and present analysis and visualisations tailored to your audience

Developing data science capability

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
  • support data science capability building across the team and wider organisation
  • confidently talk about the benefits of data science approaches to existing and potential customers
  • demonstrate a good understanding of a range of data science techniques, such as machine learning and natural language processing, and you can use them to build data science solutions, including reports, models and dashboards

Ethics and privacy (data science)

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • show an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders
  • stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area
  • identify and respond to ethical concerns in your area of responsibility

Programming and build (data science)

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • write moderate to complex programs and scripts
  • show a good understanding of testing methodologies and how to deploy code

Understanding product delivery

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • understand the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product
  • define the minimum viable product (MVP) and support decisions about priorities
  • work with specialists in multidisciplinary teams to smoothly deliver data science products into the organisation

4. Principal data scientist

A principal data scientist is a leader of data science, quite often with responsibility for managing and developing teams.

At this role level, you will:

  • have a broad knowledge of data science techniques, use cases and potential impact, as well as the tools and technologies
  • have extensive experience in scoping, designing and delivering data science outputs and products
  • work collaboratively with a range of experts in support of organisational objectives
  • communicate effectively and challenge delivery plans and priorities
  • appreciate and understand data ethics, data preparation and manipulation
  • appreciate and understand delivery methods, and how to deliver supported solutions at scale

This role level is often performed at the Civil Service job grade of:

  • SEO (Senior Executive Officer)
  • G7 (Grade 7)
Skill Description

Applied maths, statistics and scientific practices

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively
  • critique statistical analyses
  • use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle
  • use model outputs to produce evidence and help design services and policies
  • understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them

Data engineering and manipulation

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organisation effectively
  • understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place
  • understand a broad range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation, and can use these tools appropriately and help others use them

Data science innovation

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • be a leader in the data science space
  • demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for your team
  • act as a coach, inspiring curiosity and creativity in others
  • demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry
  • challenge the status quo and always look for ways to improve data science

Delivering business impact

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • lead and support your organisation area by using data science to create change
  • identify opportunities to develop data science products to support organisational objectives, while collaborating across the organisation to fulfil goals
  • show an understanding of the role of user research, and can design and manage processes to gather and establish user needs
  • communicate relevant and compelling stories effectively and present analysis and data visualisations clearly to get across complex messages
  • work with colleagues to implement scalable data science products, and to understand maintenance requirements

Developing data science capability

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • manage your CPD (continuous professional development) and can link your learning to objectives and organisational goals
  • support data science capability building across the team and wider organisation
  • confidently talk about the benefits of data science approaches to existing and potential customers
  • demonstrate a good understanding of a range of data science techniques, such as machine learning and natural language processing, and you can use them to build data science solutions, including reports, models and dashboards

Ethics and privacy (data science)

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • show an understanding of how ethical issues fit into a wider context and can work with relevant stakeholders
  • stay up to date with developments in data ethics standards and legislation frameworks, using these to improve processes in your work area
  • identify and respond to ethical concerns in your area of responsibility

Programming and build (data science)

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • write complex programs and scripts
  • seek to make code open source where appropriate
  • supervise junior analysts and set coding standards for your team
  • understand software architecture and how to write efficient, optimised code
  • perform user testing on products prior to launch

Understanding product delivery

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • understand the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product
  • define the minimum viable product (MVP) and support decisions about priorities
  • work with specialists in multidisciplinary teams to smoothly deliver data science products into the organisation

5. Lead data scientist

A lead data scientist is a leader, coach, and champion of data science. They have responsibility for managing and developing teams, building organisational capability, and setting and communicating direction.

At this role level, you will:

  • have experience in identifying opportunities to deploy extensive knowledge of data science techniques, and in challenging the status quo
  • design and implement scalable data science outputs and products, driving collaboration with others in support of organisational objectives
  • select the most appropriate tools and technologies
  • navigate ethical and data challenges
  • communicate and present effectively
  • challenge delivery plans and priorities

This role level is often performed at the Civil Service job grade of:

  • G7 (Grade 7)
  • G6 (Grade 6)
Skill Description

Applied maths, statistics and scientific practices

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively
  • critique statistical analyses
  • use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle
  • use model outputs to produce evidence and help design services and policies
  • understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them

Data engineering and manipulation

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • help to identify the data engineering requirements for any data science product, while working with data engineers and data scientists to design and deliver those products into the organisation effectively
  • understand the need to cleanse and prepare data before including it in data science products and can put reusable processes and checks in place
  • understand a broad range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation, and can use these tools appropriately and help others use them

Data science innovation

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • be a leader in the data science space
  • demonstrate in-depth knowledge of data science tools and techniques, which you can use to solve problems creatively and to create opportunities for your team
  • act as a coach, inspiring curiosity and creativity in others
  • demonstrate in-depth knowledge of your chosen profession and keep up to date with changes in the industry
  • challenge the status quo and always look for ways to improve data science

Delivering business impact

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • champion the role of data science within the organisation
  • understand and champion user research, and can design and manage processes to gather and establish user needs
  • identify and create opportunities to develop and deliver data science products to support organisational objectives, while collaborating across the organisation to fulfil meaningful goals
  • take responsibility for delivering scalable data science products into the organisation, and establishing maintenance support

Developing data science capability

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • act as a leader or technical specialist, providing detailed support and guidance within the organisation and helping colleagues to develop skills
  • set the direction of CPD (continuous professional development) within your team
  • keep up to date with new developments in data science and can match those to opportunities in your organisation
  • talk confidently about the benefits of data science approaches to existing and potential customers
  • demonstrate an in-depth understanding of a wide range of data science techniques, such as machine learning and natural language processing, and detailed knowledge of at least one specialism
  • use these techniques to build data science solutions, including reports, models and dashboards

Ethics and privacy (data science)

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • oversee compliance with data ethics standards and legislation
  • develop and manage the ethical framework for how data, machine learning and artificial intelligence techniques are used across the organisation, ensuring data governance complies with relevant legislation and standards
  • embed a culture of data ethics and explain why this is so important
  • ensure ethics guidance is appropriately applied to the formulation, implementation and evaluation of policies and programmes
  • assess and constructively challenge proposed policies and programmes

Programming and build (data science)

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • write complex programs and scripts
  • seek to make code open source where appropriate
  • supervise junior analysts and set coding standards for your team
  • understand software architecture and how to write efficient, optimised code
  • perform user testing on products prior to launch

Understanding product delivery

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • understand the differences between delivery methods, such as Agile and waterfall, and can set out how your team should use and adapt these methods
  • lead a team through the different phases of the product delivery life cycle
  • collaborate with the product manager to influence the direction of work
  • identify and involve relevant teams to smoothly deliver data science products into the organisation, ensuring these products inform decision making
  • ensure products are monitored, maintained and continually improved, engaging and working with others where necessary
  • have oversight of any data science features implemented within products or services

6. Head of data science

The head of data science has complete oversight of data science within their organisation.

At this role level, you will:

  • set direction
  • build capability
  • oversee resourcing, budgeting, professionalism and outputs and products
  • support and enable future IT developments
  • understand and use a wide range of data science techniques, tools and technologies
  • lead on ethics
  • communicate and present data science and data ethics effectively to ministers and senior leaders
  • champion the role of data science in supporting organisational priorities, and in collaborative working across professions
  • represent the department on data science matters

This role level is often performed at the Civil Service job grade of:

  • G6 (Grade 6)
Skill Description

Applied maths, statistics and scientific practices

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • identify opportunities to develop statistical insight, reports and models to support organisational objectives, while collaborating across the organisation effectively
  • critique statistical analyses
  • use a variety of data analytics techniques (such as data mining and prescriptive and predictive analytics) for complex data analysis through the whole data life cycle
  • use model outputs to produce evidence and help design services and policies
  • understand a broad range of statistical tools, particularly those deployed within the organisation, and can use these appropriately and help others to use them

Data engineering and manipulation

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • work with data engineers and data scientists to design and deliver products into the organisation effectively
  • understand the reasons for cleansing and preparing data before including it in data science products and can put reusable processes and checks in place
  • access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation

Data science innovation

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • demonstrate practical knowledge of data science tools and techniques
  • develop data science solutions that maximise insight
  • identify opportunities for how data science can improve data practices

Delivering business impact

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • champion the role of data science within the organisation
  • understand and champion user research, and can design and manage processes to gather and establish user needs
  • identify and create opportunities to develop and deliver data science products to support organisational objectives, while collaborating across the organisation to fulfil meaningful goals
  • take responsibility for delivering scalable data science products into the organisation, and establishing maintenance support

Developing data science capability

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • act as a leader or technical specialist, providing detailed support and guidance within the organisation and helping colleagues to develop skills
  • set the direction of CPD (continuous professional development) within your team
  • keep up to date with new developments in data science and can match those to opportunities in your organisation
  • talk confidently about the benefits of data science approaches to existing and potential customers
  • demonstrate an in-depth understanding of a wide range of data science techniques, such as machine learning and natural language processing, and detailed knowledge of at least one specialism
  • use these techniques to build data science solutions, including reports, models and dashboards

Ethics and privacy (data science)

Level: expert

Expert is the fourth of 4 ascending skill levels

You can:

  • oversee compliance with data ethics standards and legislation
  • develop and manage the ethical framework for how data, machine learning and artificial intelligence techniques are used across the organisation, ensuring data governance complies with relevant legislation and standards
  • embed a culture of data ethics and explain why this is so important
  • ensure ethics guidance is appropriately applied to the formulation, implementation and evaluation of policies and programmes
  • assess and constructively challenge proposed policies and programmes

Programming and build (data science)

Level: working

Working is the second of 4 ascending skill levels

You can:

  • write and test scripts and create basic models in one or more languages
  • collaborate on shared codebases, using a variety of methodologies

Understanding product delivery

Level: practitioner

Practitioner is the third of 4 ascending skill levels

You can:

  • understand the differences between delivery methods, such as Agile and waterfall, and can choose the most appropriate method to deliver each product
  • define the minimum viable product (MVP) and support decisions about priorities
  • work with specialists in multidisciplinary teams to smoothly deliver data science products into the organisation
Role Shared skills
Machine learning engineer

Applied maths, statistics and scientific practices

Data science innovation

Ethics and privacy (data science)

Updates

Published 7 January 2020

Last updated 30 August 2022

30 August 2022

  • Data scientist was fully updated. All role levels and their respective skills have been defined.

25 February 2021

  • The data scientist role has an up to date summary of responsibilities. There is more information about the role of a data scientist in government and the most up to date job model on the Government Analyst Career Framework.

7 January 2020

  • First published.