PPA Energy Modelling Analyst

Job Description

Based in Oxford, you will contribute to our energy modelling efforts by developing analytical and computational tools, enhancing our modelling methodology, analysing data, formulating recommendations on future trends, and conveying insights to enhance clients’ decision making.

This position has a focus on modelling pricing models for energy financing (notably PPAs: power purchase agreements). This role will suit a recent university graduate (MSc or PhD), with a strong mathematical inclination and an interest in stochastic or statistical (pricing) models.

Key Responsibilities

• Assist in the development of a cutting-edge pricing engine for PPA products, including pricing, calibration, and data preparation;
• Collaborate closely with our research and advisory teams to derive insights from model results, ensuring that they enhance our ability to accurately price complex financial products;
• Customise and adapt pricing models to reflect the intricacies of specific energy markets, while building analytical tools to enhance Aurora’s technical suite;
• Actively contribute to the continuous improvement of processes within the broader modelling team.

Skills, Knowledge and Expertise

Required attributes:

• Relevant degree in Economics, Engineering, Mathematics, Computer Science or other quantitative field;
• Exceptional problem solving skills and analytical ability;
• Ability to collect, analyse and interpret complex quantitative data and information;
• Knowledge of one or more programming languages, e.g. C++, Matlab, Python, R, Java;
• Strong interpersonal skills, and a great team player.

Desirable attributes:

• Relevant Master’s degree or PhD;
• Knowledge of and interest in energy markets, and a belief that well-designed models significantly improve decision making;
• Knowledge of statistical or stochastic modelling;
• Interest in pricing, or the financial instruments used in energy trading (e.g. futures, forwards, options, swaps);
• Familiarity in software engineering best practice (version control, automatic testing, writing modular code), and cloud-based deployment techniques.