RWE Offshore Wind GmbH, Essen, Hamburg, Swindon, Coventry, London, Barcelona, Chicago, Madrid, Paris, Rome, Austin, Warszawa, Milano, Tokyo, Melbourne

To start as soon as possible, Full time, Permanent

Functional area: Commercial / Trading

The Team Lead Quantitative Methods will lead the Quantitative Methods team in Global Modelling & Methods in the RWE Renewables Commercial Analysis Department. The Global Modelling & Methods team is responsible for all quantitative modelling activities within RWE Renewables Commercial area including developing, running and maintaining a range of consistent and best-in-class renewables and energy storage portfolio optimisation, risk analysis, pricing and forecasting models, covering all RWE Renewables markets in Americas, EU and APAC (Asia Pacific), as such the role offers the opportunity for the right candidate to travel regularly to different business regions, and work among a diverse range of cultures.

Among the key responsibilities of the role will be responsibility for the modelling of commercial revenue streams of energy storage technologies, mainly battery storages, stand alone or paired with renewable assets, covering both long term for investment decision support and short term for asset commercialisation in all core and future markets of RWE Renewables.

Another main responsibility of the role will be leading the development models and methods for modelling Ancillary Service, imbalance, and Intraday markets. This work enables us to quantify the impact of regulatory changes on RWE Renewables portfolio revenues, and value new investments in renewable energy assets, Onshore and Offshore Wind, PV, and Storage.

The role holder will have the responsibility to embed these models and methods across the global business, working with local regional stakeholders. Alignment with other regional operations (Europe, Americas, APAC), Group Centre as well as the local business in Spain will be necessary to fulfil these responsibilities.

The role holder should be able to transform the results of complex quantitative analysis into easily understandable recommendations and deliver to senior stakeholders ensuring well-grounded, model driven decisions. The role has a direct impact on implementation of commercial strategies regarding hedging of RWE renewables portfolio, optimal asset / location selection for both investment and divestment decisions.

Principal Accountabilities

• Lead the quantitative support and model driven recommendations for final investment decisions (FIDs), power purchase agreements (PPAs) and M&A transactions.
• Lead on the analysis, development & maintenance of algorithms and optimisation models for renewables portfolio, standalone or hybrid battery storage revenues using linear/non-linear programming techniques.
• Recruitment, and development of a team of quantitative analysts.
• Lead Project work streams regarding commercial portfolio strategy.
• Evaluate commercial opportunities of new emerging technologies such as Hydrogen and Power-to-X and their impact of RWE Renewables portfolio revenues with bespoke fundamental / econometric models.
• Work with Group and Renewables Risk Controlling to ensure that developed models are certified, well documented and best in class.
• Research new methodologies and areas of model applications and identify potential improvements to current models, ensure new code meets the team standards for production.
• Support Commercial Functions (Commercial Analysis, Structured Origination, Optimisation, and CCO) with ad-hoc modelling and commercial expertise.

Required Skills & Competencies

• Undergraduate degree in an analytical / numerical subject, preferably Operations Research, Engineering, Economics, Statistics, Mathematics. (Masters’ or PhD degree would be advantageous)
• Excellent analytical and problem solving skills, attention to detail and strong mathematical understanding.
• Willingness to travel regularly, to other regional locations and group centre in Germany.
• Very strong Excel i.e. should be able to use all excel functionality at an expert level, writing macros and VBA Functions, integrating excel with external data sources.
• Demonstrable development experience using one of: Python, R, Matlab, C/C++, Go, Julia, with an emphasis on data analysis and modelling, in a professional or academic background.
• Several years of relevant academic or professional experience in a position developing models focused on optimisation, quantitative finance, or data science in finance or energy sectors.
• Strong interpersonal and presentational skills (expert user of PowerPoint) plus ability to deal with uncertainty linked to cross-cultural projects.
• Effective written and verbal communication skills; fluency in English (verbal and written) is essential. (A second language would be advantageous)
• Ability to explain and defend the methodology used in the development of quantitative models and tools.
• Highly proactive, identifying value opportunities and proposing solutions to them under own initiative.

rwe.com/career

Apply now with just a few clicks: ad code 79447

Any questions? Christopher Kretschmann, christopher.kretschmann.extern@rwe.com

We look forward to receiving your application!

We value diversity and therefore welcome all applications, irrespective of gender, disability, nationality, ethnic and social background, religion and beliefs, age or sexual orientation and identity.

Of course, you can find us on LinkedIn, Twitter and Xing, too.

Renewables is the future of our business, and Offshore wind a pivotal element of our carbon neutral ambition. With 800+ fixed bottom and floating turbines – plus others totalling 800MWh we operate for partners – we’re already the No.2 generator globally. And we plan to triple our 2.2GWh output by 2030, led by landmark projects such as Sofia (1,400 MWh) and Kaskasi (342MWh). Offshore wind is a constantly evolving technical, practical and social challenge. Our financial stability means you’ll have the freedom to act with conviction and courage as we pivot to harness new technologies – and continuously improve, together.