Data scientist
Data scientists use statistics, machine learning, and other advanced methods to find patterns in health and care data and to build models that help services plan, predict, and improve care. Typical work includes forecasting demand, identifying patients who may benefit from early support, and evaluating the effect of service changes. They work closely with clinicians and analysts, and make sure models are valid, fair, and used safely.
- Family
- Data
- UK GDaD PCF role
- Data scientist (Data)
- ESCO occupations
- data scientist (ISCO-08 2511); statistician (ISCO-08 2120)
- On GitHub
- Role document · Role data
Levels
| Band | Role level | UK GDaD PCF level | Civil Service grades | Job evaluation points |
|---|---|---|---|---|
| 4 | Trainee data scientist | Trainee data scientist | AO/EO | 276 |
| 5 | Associate data scientist | Associate data scientist | EO/HEO | 350 |
| 6 | Data scientist | Data scientist | HEO/SEO | 427 |
| 7 | Principal data scientist | Principal data scientist | SEO/G7 | 507 |
| 8a | Lead data scientist | Lead data scientist | G7/G6 | 560 |
| 8b | Head of data science | Head of data science | G7/G6 | 603 |
In a digital health care organization
- A model that supports decisions about patients can cause harm if it is wrong or biased, so models are validated on the population they will be used with and assessed for clinical safety.
- Health data reflects unequal access to care, so models can repeat or worsen health inequalities unless bias is measured and addressed for each patient group.
- Some models, such as those that inform diagnosis or treatment, may be regulated as medical devices, and AI regulation may also apply.
- Data science often needs linked, detailed patient data, so work happens in secure settings, with pseudonymized data, under an agreed information governance basis.
- Clinicians and patients need to understand and trust a model's outputs, so explanation and plain-language communication of uncertainty matter.
UK GDaD PCF role description
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
ESCO occupations and skills
ESCO lists the skills and knowledge that employers usually need (essential) or may need (optional) for each occupation. Use them as a checklist for development conversations.
data scientist
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Essential skills and knowledge (63)
Skills and competences
- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- build recommender systems
- collect ICT data
- communicate with a non-scientific audience
- conduct research across disciplines
- deliver visual presentation of data
- demonstrate disciplinary expertise
- design database scheme
- develop data processing applications
- develop professional network with researchers and scientists
- disseminate results to the scientific community
- draft scientific or academic papers and technical documentation
- establish data processes
- evaluate research activities
- execute analytical mathematical calculations
- handle data samples
- implement data quality processes
- increase the impact of science on policy and society
- integrate gender dimension in research
- interact professionally in research and professional environments
- interpret current data
- manage data collection systems
- manage findable accessible interoperable and reusable data
- manage intellectual property rights
- manage open publications
- manage personal professional development
- manage research data
- mentor individuals
- normalise data
- operate open source software
- perform data cleansing
- perform project management
- perform scientific research
- promote open innovation in research
- promote the participation of citizens in scientific and research activities
- promote the transfer of knowledge
- publish academic research
- report analysis results
- speak different languages
- synthesise information
- think abstractly
- use data processing techniques
- use databases
- write scientific publications
Knowledge
- data engineering
- data ethics
- data mining
- data models
- data science
- data visualisation software
- empirical analysis
- information categorisation
- information extraction
- mathematical modelling
- online analytical processing
- quantitative analysis
- query languages
- resource description framework query language
- scientific literature
- statistical modeling techniques
- statistics
- visual presentation techniques
Optional skills and knowledge (34)
Skills and competences
- apply blended learning
- create data models
- define data quality criteria
- design database in the cloud
- integrate ICT data
- make data-driven decisions
- manage ICT data architecture
- manage ICT data classification
- manage data
- perform data mining
- teach in academic or vocational contexts
- use spreadsheets software
Knowledge
- Hadoop
- LDAP
- LINQ
- MDX
- N1QL
- SPARQL
- XQuery
- business analytics
- business intelligence
- computational biology
- computer simulation
- data quality assessment
- digital curation
- healthcare analytics
- image recognition
- marketing analytics
- multidisciplinary research
- research design
- scientific computing
- social network analysis
- state estimation
- unstructured data
statistician
Statisticians collect, tabulate, and, most importantly, analyse quantitative information coming from a varied array of fields. They interpret and analyse statistical studies on fields such as health, demographics, finance, business, etc. and advise based on patterns and drawn analysis.
Essential skills and knowledge (50)
Skills and competences
- apply for research funding
- apply research ethics and scientific integrity principles in research activities
- apply scientific methods
- apply statistical analysis techniques
- communicate with a non-scientific audience
- conduct quantitative research
- conduct research across disciplines
- demonstrate disciplinary expertise
- develop professional network with researchers and scientists
- disseminate results to the scientific community
- draft scientific or academic papers and technical documentation
- evaluate research activities
- execute analytical mathematical calculations
- gather data
- identify statistical patterns
- increase the impact of science on policy and society
- integrate gender dimension in research
- interact professionally in research and professional environments
- manage findable accessible interoperable and reusable data
- manage intellectual property rights
- manage open publications
- manage personal professional development
- manage research data
- mentor individuals
- operate open source software
- perform data analysis
- perform project management
- perform scientific research
- process data
- promote open innovation in research
- promote the participation of citizens in scientific and research activities
- promote the transfer of knowledge
- publish academic research
- speak different languages
- synthesise information
- think abstractly
- write scientific publications
Knowledge
- data ethics
- data quality assessment
- data science
- mathematical modelling
- mathematics
- quantitative analysis
- research design
- scientific literature
- scientific research methodology
- statistical analysis system software
- statistical modeling techniques
- statistics
Other
Optional skills and knowledge (35)
Skills and competences
- advise on financial matters
- advise on legislative acts
- analyse big data
- apply blended learning
- assist scientific research
- build predictive models
- carry out statistical forecasts
- conduct public surveys
- deliver visual presentation of data
- design questionnaires
- develop financial statistics reports
- develop scientific theories
- develop statistical software
- manage database
- manage quantitative data
- plan research process
- prepare lesson content
- present reports
- provide lesson materials
- teach in academic or vocational contexts
- use mathematical tools and equipment
- use spreadsheets software
- write research proposals
Knowledge
Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.