Non-Commodity Pricing Specialist

Non-Commodity Pricing Specialist, West London/Hybrid, £80,000 - £90,000 per annum

A good client of mine, a leading independent B2B energy supplier are looking for analytical, curious, and commercially minded people to join their Commercial team and take ownership of how they forecast, model, and manage non-commodity costs.

Your insights will directly influence their pricing, profitability, and the value they deliver to some of the UK's most complex energy users.

Here's what a typical week might look like:

• Forecast and model non-commodity costs across electricity and gas portfolios (Distribution, Transmission, Environmental & Government Levies etc)
• Keep our pricing models accurate, scalable, and compliant with changing market and regulatory frameworks.
• Partner with Sales, Operations, Finance and Regulatory teams to ensure non-commodity inputs are correctly reflected in pricing, billing, and reconciliations.
• Support large-deal pricing and bespoke tenders by providing clear, data-backed insights to account managers and senior stakeholders.
• Investigate and reconcile variances between forecast and billed costs, identifying drivers and proposing improvements.
• Automate repetitive processes and build smarter workflows using SQL, Python, or BI tools.
• Stay ahead of industry changes - network charging reforms, regulatory consultations, and system operator updates - and help the team adapt proactively.
• Create clear, engaging reports and dashboards to communicate findings and market movements.

What we're looking for

We're looking for individuals with different levels of experience who possess analytical edge and energy market awareness to make data meaningful, and an individual who has the confidence to influence commercial decisions.

You'll have:

• 2-6 years' experience in energy pricing, forecasting, settlements, or commercial analytics depending on where you are at in your career.
• A solid grasp of GB electricity and/or gas non-commodity charges.
• Advanced Excel skills and comfort working with large, complex datasets.
• Experience with SQL, Python, or similar tools for analysis and automation.
• Attention to detail, a structured approach to problem-solving, and strong commercial awareness.
• The ability to explain analytical findings in plain language and collaborate across teams.