Data scientist

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

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

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

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

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

You can:

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

You can:

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

You can:

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

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

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

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

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

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

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

You can:

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

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

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

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

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

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

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

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

You can:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

You can:

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

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

Applied maths, statistics and scientific practices

Data science innovation

Ethics and privacy (data science)