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Job Description:
At Shell Energy UK, we believe there’s a better way to power your business.
Aligned with Shell’s ambition to be a significant global power business and to become net zero by 2050, we are here to guide businesses to a better energy future. One that has the customer and a commitment to cleaner energy at the heart and makes it easy and affordable to embrace new technology.
We offer businesses across Britain 100% renewable electricity as standard. Our cleaner, affordable and simple solutions help companies manage their energy spend and plan their sustainability roadmap - a fresh approach for environmentally conscious businesses that are looking to boost their sustainability credentials. We also offer natural gas to our customers with the option of supplying green gas on request.
As a Demand Forecasting Analyst, you will:
Proactively review Non-Flex portfolio energy usage, recommending changes to forecasting models and methodology where appropriate.
Produce MAPE analysis of the Non-Flex portfolio, suggesting changes to the forecast where a long/short bias is evident.
Process customer reforecasts timely and support the E2E reforecast journey.
Serve as a key point of contact for both internal and external stakeholders at varying levels of seniority.
As a member of the wider UK B2B demand forecasting team, you will also have the opportunity to:
Support ongoing process development within the UK B2B DF team.
Participate in process change projects within the wider business.
Requirements
Essential:
Understanding of external factors that can impact energy usage.
Knowledge of B2B energy products (flex, fixed, etc.).
Proficiency in SQL and Microsoft Excel.
Experience with large data sets.
Strong communication skills, particularly the ability to convey complex issues to those with varying levels of understanding.
Attention to detail and ability to identify data anomalies.
Desirable:
Energy forecasting or position management experience.
Experience creating and maintaining PowerBI dashboards, especially in a management-information (MI) context.
Experience with data modeling in R.
Experience in user-acceptance testing and writing user-acceptance test scripts.
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