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Data Scientist

Vallum Associates
Sheffield
1 month ago
Applications closed

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Data Scientist - Outside IR35 - Energy Sector


  • 3 months initial contract
  • Outside IR35
  • Remote based

Our client as a business segment that currently lacks robust, forward-looking insights into how their customer portfolio is likely to evolve over the coming years. Sales strategies and negotiations are heavily reliant on individual salesperson experience rather than predictive analytics. Additionally, the Customer Services team is structured reactively based on current conditions, rather than proactively aligned to future portfolio needs.


Data Scientist Objective

To scope and define how advanced data science capabilities can enhance forecasting and strategic planning within the Mid-Market segment. The aim is to:

  • Reduce customer churn through predictive modelling.
  • Improve targeting and prioritisation of sales efforts.
  • Enable proactive resource planning for Customer Services.


Data Scientist Scope of Work

The consultant will lead a scoping exercise to identify and prototype data science solutions that address the following (but not limited to):

  • Churn prediction: Identify customers at risk of leaving and the drivers behind churn.
  • Sales forecasting: Model future sales volumes and customer acquisition trends.
  • Resource forecasting: Align operational resources with expected portfolio changes.

This work will inform future investment in data science capabilities and operational planning.


We are seeking a Data Scientist with:

  • Proven experience translating complex business challenges into actionable data science solutions.
  • Ability to work independently with minimal internal support.
  • Strong stakeholder engagement skills, particularly with commercial and operational teams.
  • Experience in the energy sector or B2B environments is desirable.

Technical Environment

  • Data platform: Snowflake
  • Production environment: Clients internal Data platform and AWS-based tools
  • Data access will be arranged in collaboration with internal teams.

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