Data Scientist

Job Description:

Data scientists use software, artificial intelligence and machine learning to analyse and interpret large amounts of data

Job Category:
Government & Public Services

What you will do:

Day-to-day tasks could include:

  • use data to identify, manage and solve business problems
  • gather and manage data from different sources to create models and test hypotheses
  • manipulate, analyse and visualise data using statistical software
  • produce recommendations and explain them to different audiences
  • build complex mathematical and analytical models using algorithms and machine learning techniques
  • identify and explain how AI and data science can benefit your organisation
  • keep up to date with developments in data science and AI


You’ll need:

  • knowledge of maths and statistics
  • knowledge of computer operating systems and software packages like R or Python
  • an understanding of how businesses are managed and operated
  • to be able to use a computer and the main software packages competently

As well as:

  • the ability to think clearly using logic and reasoning
  • excellent verbal communication skills
  • strong analytical and problem-solving skills
  • a curious and inquisitive mindset
  • to be thorough and pay attention to detail (organisational skills)
  • excellent written communication skills – the ability to communicate complex findings to non-technical audiences
  • continuous learning and staying updated with the latest trends and technologies in data science are essential for success in this dynamic field (adaptable)
Illustration of employee looking at workspace

Entry Requirements:

You can get into this job through a:

  • university course
  • an apprenticeship
  • working towards
  • a graduate training scheme

With a relevant degree or postgraduate qualification, you can apply for graduate training schemes in AI and data science. Particularly relevant subjects include:

  • maths
  • statistics
  • computer science
  • data science
  • operational research

Subjects that teach high-level statistics, like physics, engineering or psychology may also be useful.

Graduates of other subjects may still be able to enter AI and data science, for example, by doing a master’s conversion course.

People from backgrounds that are under-represented in the profession, may be able to get financial support through a scholarship to do this.

Work experience through internships and year in industry placements will give you an advantage when looking for jobs.

Try to get experience of relevant coding, analysis and data manipulation software packages like:

  • R
  • SQL
  • Python
  • Power BI
  • Excel

You may be able to get into this job through a degree apprenticeship.

Relevant apprenticeships include:

  • data scientist integrated degree apprenticeship
  • artificial intelligence (AI) data specialist higher apprenticeship
  • digital and technology solutions specialist degree apprenticeship

It may be possible to start in an entry-level job that involves working with data and work your way up into a data scientist position. You could do this by doing work-based qualifications or teaching yourself through online learning.

You would need to show an interest in solving problems and understanding how businesses work. Knowledge of maths and statistics, and some experience of computer operating systems would also be useful.

Other Routes
Data scientists work in lots of different sectors and often you can transfer the skills you develop between these sectors. You may be able to find trainee roles or graduate schemes with:

  • government departments
  • healthcare organisations
  • finance and professional services firms
  • IT companies
  • retail and sales organisations
  • university research departments

School Subjects

To become a Data Scientist in the UK, specific GCSE subjects are not mandatory, but certain subjects can be beneficial in providing a foundation for the necessary skills and knowledge in this field, such as:

  1. Mathematics: Strong mathematical skills are essential for understanding statistical concepts, algebra, calculus, and data analysis techniques.
  2. Computer Science: Knowledge of computer programming and coding languages, such as Python or R, is crucial in data analysis and machine learning.
  3. Physics: Physics can provide a foundation for understanding scientific modeling and complex data systems.
  4. Statistics: Studying statistics can help in understanding probability, hypothesis testing, and other statistical methods used in data analysis.
  5. ICT (Information and Communication Technology): Knowledge of ICT is valuable for working with data visualization tools and databases.
  6. Business Studies or Economics: These subjects can provide insights into real-world applications of data analysis in business and economics.

Working Hours and Environment:

You could work in an office or remotely –  working typically 37-39 hours a week

Career Path & Progression:

You could specialise in a specific area like:

  • artificial intelligence
  • machine learning
  • database management

You could move into a more senior data scientist role or take on responsibility for people or project management.

You could move between different sectors or go into academic research and teaching.

You may be able to work as a freelance consultant.