Illustrative Reference profiles for a generic digital health care organisation, not official job descriptions. About this reference.

Applied maths, statistics and scientific practices

Applied mathematics and statistics involves applying analytical methods including exploratory data analysis, visualisation and statistical testing to help make accurate recommendations.

Quoted from the UK GDaD PCF.

Levels

1. Awareness

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
2. Working

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
3. Practitioner

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
4. Expert

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

Contains public sector information from the UK Government Digital and Data Profession Capability Framework (https://understand-digital-data-roles-skills.service.gov.uk/), licensed under the Open Government Licence v3.0. © Crown copyright.

Closest ESCO skills

Contains ESCO v1.2.1 data (https://esco.ec.europa.eu/). © European Union. Reuse authorised under Commission Decision 2011/833/EU.

Role levels that expect this skill

Role levelRoleBandExpected level
Trainee data scientistData scientist4Awareness
Associate data scientistData scientist5Working
Data scientistData scientist6Practitioner
Principal data scientistData scientist7Expert
Lead data scientistData scientist8aExpert
Head of data scienceData scientist8bExpert
Senior machine learning engineerMachine learning engineer7Practitioner
Lead machine learning engineerMachine learning engineer8bPractitioner