Job Description

Key Responsibilities

Monitor & Analyse Network Performance:
Track utilisation, uptime, session duration, and throughput across the EV charging network using data from charge point management systems (CPMS), telemetry, and CRM tools.
Optimise Site Performance:
Identify underperforming sites and propose actionable improvements related to energy supply, pricing, location, or equipment configuration.
Data Modelling & Forecasting:
Build predictive models to forecast demand growth, charging behavior, and energy costs to support investment decisions and DNO capacity planning.
Revenue & Cost Analysis:
Analyse tariff structures, grid costs, and energy consumption to optimise site profitability and sustainability.
Data Quality & Integration:
Develop and maintain data pipelines, dashboards, and automated reports using BI tools (Power BI, Tableau, or Looker) to visualise KPIs for stakeholders across operations, commercial, and engineering teams.
Cross-Functional Collaboration:
Work closely with network operations, engineering, fleet, and property teams to align insights with strategic site development and asset optimisation.
Continuous Improvement:
Develop new metrics and frameworks for measuring performance, including carbon impact, utilisation per connector, and return on infrastructure investment.

Skills & Experience

Essential:

Proven experience as a Data Analyst, ideally within EV charging, renewable energy, or utilities.
Experience working with large, real-time datasets.
Strong analytical and problem-solving skills with attention to detail.
Ability to translate data insights into actionable business recommendations.

Desirable:

Experience with energy pricing, load balancing, or DNO connection capacity data.
Knowledge of EV charging infrastructure, OZEV grant schemes, or CPMS platforms.
Familiarity with machine learning or predictive analytics for demand forecasting.
Degree in a relevant field (Data Science, Engineering, Economics, or similar).

EV Optimisation Analyst

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