Guidance

Head of data science: skills they need

Updated 2 January 2019

This content is part of the Digital, Data and Technology (DDaT) Capability Framework which describes the DDaT roles in government and the skills needed to do them.

1. What a head of data science does

A head of data science provides leadership and direction across a programme of multidisciplinary data science projects, managing resources to ensure delivery.

Heads of data science:

• are recognised as strategic authorities with technical expertise in cutting edge techniques, defining vision across the organisation
• are role models to other data scientists and champion adoption of best practice
• communicate with senior stakeholders and convince them of the strategic value of applying data science

2. What skills they need

A head of data science needs specific technical skills.

All roles have essential skills, and some have desirable skills.

Each skill has one of 4 skill levels associated with it:

• Expert
• Practitioner
• Working
• Awareness

2.1 Essential skills

Skill	Description of the skill	Skill level	What the skill level means
Applied maths, statistics, and scientific practices	Understands how algorithms are designed, optimised and applied at scale. Can select and use appropriate statistical methods for sampling, distribution assessment, bias and error. Understands problem structuring methods and can evaluate when each method is appropriate. Applies scientific methods through experimental design, exploratory data analysis and hypothesis testing to reach robust conclusions.	Practitioner	Understands and can help teams apply a range of practices. Develops deeper expertise on a narrower range of specialisms. Starts to apply emerging theory to practical situations.
Data engineering and manipulation	Works with other technologists and analysts to integrate and separate data feeds in order to map, produce, transform and test new scalable data products that meet user needs. Has a demonstrable understanding of how to expose data from systems (for example, through APIs), link data from multiple systems and deliver streaming services. Works with other technologists and analysts to understand and make use of different types of data models. Understands and can make use of different data engineering tools for repeatable data processing and is able to compare between different data models. Understands how to build scalable machine learning pipelines and combine feature engineering with optimisation methods to improve the data product performance.	Practitioner	Can work with data engineers to map, produce, transform and test new data feeds for data owners and consumers, selecting the most appropriate tools and technologies. Can lead ad hoc data exploration in a wide variety of data serialisation and storage formats, from across the business, for data consumers.
Data science innovation	Recognises and exploits business opportunities to ensure more efficient and effective ways to use data science. Explores ways of utilising new data science tools and techniques to tackle business and organisational challenges. Demonstrates strong intellectual curiosity with an interdisciplinary approach, drawing on innovation in academia and industry.	Practitioner	Displays strong intellectual curiosity and proactively explores areas of innovation in both government and industry. Can identify the business value for innovation within their organisation.
Developing data science capability	Continuously develops data science knowledge, utilising multiple sources. Shares data science practices across departments and in industry, promoting professional development and use of best practice across all capabilities identified for data scientists. Focuses on recruitment and induction of data scientists.	Expert	Advocates the importance of continuous learning to the data science team and propagates data science capability to the wider organisation and in industry. Is a role model of best practice through demonstration of their own self-directed learning. Sets the data science curriculum across government.
Domain expertise	Understands the context of the business, its processes, data and priorities. Applies data science techniques to present, communicate and disseminate data science products effectively, appropriately and with high impact. Uses the most appropriate medium to visualise data to tell compelling and actionable stories relevant for business goals. Maintains a user focus to design solutions that meet user needs, taking account of agreed cross-government ethics standards.	Expert	Communicates the business benefit of data science products, championing and governing these across the organisation. Can communicate relevant, compelling stories in the most appropriate medium. Ensures data governance and data ethics are embedded in organisational strategy. Aligns data science priorities with wider organisational objectives (for example, budget).
Programming and build (data science)	Uses a range of coding practices to build scalable data products that can be used by strategic or operational users and can be further integrated into business systems. Works with technologists to design, create, test and document these data products. Works in accordance with agreed software development standards, including security, accessibility and version control.	Working	Develops, codes, tests, corrects and documents simple programmes or scripts under the direction of others as part of a multi-disciplinary team.
Understanding analysis across the life cycle (data science)	Understands the different phases of product delivery and is able to plan and run the analysis for these. Able to contribute to decision-making throughout the lifecycle. Works in collaboration with user researchers, Developers and other roles throughout the lifecycle. Understands the value of analysis, how to contribute with impact and which data sources, analytical techniques and tools can be used at each point throughout the lifecycle.	Practitioner	Understands and can help teams apply a range of techniques to analyse data and provide insight. Is proactive and can present compelling findings that inform wider decisions. Starts to apply innovative approaches to resolve problems.

3. Civil Service Success Profiles Framework

The Civil Service uses The Success Profiles Framework to assess candidates during recruitment.

It is a flexible framework, used to assess a range of experiences, abilities, strengths, behaviours and technical/professional skills required for different roles.

Find out more about Success Profiles.

4. Other roles in data science

There are 4 other role levels in data science:

• senior data scientist
• data scientist
• junior data scientist
• trainee data scientist