Data Science Degree Apprenticeship

E.ON UK PLC

Nottingham (NG1 4BX)

Closes on Monday 2 February 2026

Posted on 17 December 2025

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Summary

Embark on a four-year data science apprenticeship and you’ll be turning raw data into valuable insights, guiding the company to make informed choices that lead to real world successes. You’ll work with diverse teams across the business through six-month placements while studying part-time at Nottingham University.

Wage

£21,500 a year

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Training course
Data scientist (integrated degree) (level 6)
Hours
Monday to Friday, 9.00am to 5.00pm.

37 hours a week

Start date

Tuesday 1 September 2026

Duration

4 years

Positions available

2

Work

Most of your apprenticeship is spent working. You’ll learn on the job by getting hands-on experience.

What you'll do at work

During the programme, you'll have the opportunity to select placements based on your interests and career goals, like:

  • Using statistical analysis and visualisation tools to understand data patterns and characteristics within real data
  • Using machine learning tools and statistical techniques to produce solutions to problems
  • Using algorithms to solve problems and enthusing others to see the benefit of your work
  •  Interpreting and communicating results to stakeholders
  • Working with the data community including other data scientists and data engineers

Where you'll work

Trinity House
2 Burton Street
Nottingham
NG1 4BX

Training

Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.

Training provider

UNIVERSITY OF NOTTINGHAM, THE

Training course

Data scientist (integrated degree) (level 6)

Understanding apprenticeship levels (opens in new tab)

What you'll learn

Course contents
  • Identify and clarify problems an organisation faces, and reformulate them into Data Science problems. Devise solutions and make decisions in context by seeking feedback from stakeholders. Apply scientific methods through experiment design, measurement, hypothesis testing and delivery of results.  Collaborate with colleagues to gather requirements.
  • Perform data engineering: create and handle datasets for analysis. Use tools and techniques to source, access, explore, profile, pipeline, combine, transform and store data, and apply governance (quality control, security, privacy) to data.
  • Identify and use an appropriate range of programming languages and tools for data manipulation, analysis, visualisation, and system integration. Select appropriate data structures and algorithms for the problem.  Develop reproducible analysis and robust code, working in accordance with software development standards, including security, accessibility, code quality and version control.
  • Use analysis and models to inform and improve organisational outcomes, building models and validating results with statistical testing: perform statistical analysis, correlation vs causation, feature selection and engineering, machine learning, optimisation, and simulations, using the appropriate techniques for the problem.
  • Implement data solutions, using relevant software engineering architectures and design patterns. Evaluate Cloud vs. on-premise deployment.  Determine the implicit and explicit value of data. Assess value for money and Return on Investment.  Scale a system up/out. Evaluate emerging trends and new approaches. Compare the pros and cons of software applications and techniques.
  • Find, present, communicate and disseminate outputs effectively and with high impact through creative storytelling, tailoring the message for the audience. Use the best medium for each audience, such as technical writing, reporting and dashboards.  Visualise data to tell compelling and actionable narratives.  Make recommendations to decision makers to contribute towards the achievement of organisation goals.
  • Develop and maintain collaborative relationships at strategic and operational levels, using methods of organisational empathy (human, organisation and technical) and build relationships through active listening and trust development.
  • Use project delivery techniques and tools appropriate to their Data Science project and organisation. Plan, organise and manage resources to successfully run a small Data Science project, achieve organisational goals and enable effective change.

Training schedule

You’ll work with diverse teams across the business through six-month placements while studying part-time at the University of Nottingham for a fully funded BSc (Hons) Data Science.

Requirements

Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.

Skills

  • Communication skills
  • IT skills
  • Attention to detail
  • Organisation skills
  • Problem solving skills
  • Presentation skills
  • Number skills
  • Analytical skills
  • Logical
  • Team working
  • Creative
  • Non judgemental

About this employer

At E.ON, we're more than a provider of gas and electricity, we’re shaping a cleaner, more sustainable energy future. Our energy solutions span district heating systems, on-site generation, highways lighting and other innovative energy services that help customers take control of their energy use. Working with the latest technologies, we’re always seeking new and improved ways to shape the energy landscape.

After this apprenticeship

Further progression may well be available in other areas within our sector, upon successful completion of the apprenticeship.

Ask a question

The contact for this apprenticeship is:

UNIVERSITY OF NOTTINGHAM, THE

The reference code for this apprenticeship is VAC2000005525.

Apply now

Closes on Monday 2 February 2026

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