Data Scientist Energy Modelling

Data Scientist Energy Modelling

  • Location

    Reading, Berkshire

  • Sector:

    Data Analytics & Artificial Intelligence

  • Job type:


  • Salary:

    £45000 - £65000 per annum

  • Contact:

    Michael Winter

  • Contact email:


  • Job ref:


  • Published:

    over 1 year ago

  • Expiry date:


  • Startdate:


The Company:

An innovative and disruptive tech business is looking to expand its presence in the Thames Valley by opening up a new office in Reading, and are subsequently looking to add an experienced Data Scientist to the analytics team.
Having been at the forefront of changes within the UKs renewable energy markets for the past decade, they are looking to add advanced analytics to their platform to enhance decision making and create valuable insights to promote their low-carbon revolution of the existing energy market.


  • Proactively follow market trends to design and engineer optimum revenue strategies
  • Collate and crate insights from a diverse range of data sources
  • Create insights and reports that will be presented back to stakeholders and senior managers
  • Develop from scratch optimization algorithms that are crucial to the business
  • Build and test Machine Learning algorithms that will be used across multiple teams within the business
  • Utilise the wealth of data the company produces to make them a market leader!

Qualifications and Experience needed:

  • MSc/PhD candidate in either Statistics, Mathematics, Science or similar
  • Proven experience working within the gas & power markets
  • Strong experience coding in Python and SQL
  • Experience visualising and reporting insights using Tableau, PowerBI, Plotly etc.
  • Knowledge and experience of energy systems modelling
  • Experience with Machine Learning libraries such as Keras/Tensorflow

If this position is of interest please apply directly to this advert or contact Michael on:

Michael.winter@wademacdonald.com / 07825711262

Applications are encouraged from all candidates meeting or exceeding the minimum criteria for the role regardless of age, disability, gender, orientation, race, religion or ethnicity.