Flexible Depending on Experience
10 days ago
TGRC are working alongside a global energy firm that generates, trades and markets power on a large scale. The business procures, stores, transports and supplies commodities including nat gas, LNG and coal, alongside energy-related products. The business operates in more than 40 countries with around 40 GW of generation capacity and is one of the leading names in the energy industry globally.
The Quantitative team is responsible for developing and defining the methods that support risk quantification and optimisation decisions for all commercial and risk-taking activities. There is a strong commercial focus and responsibility to leverage its modelling best practice providing quantitative recommendations and analytics to a wide range of business stakeholders. Quantitative’s role as a competence centre in quantitative modelling is based on strong academic proficiencies, paired with clear commercial focus and the ability to apply latest technology.
Develop bespoke risk management and valuation models for one of Europe’s biggest commodity portfolio. Help the commercial functions in management and steering of market, weather and credit risk by providing Front Office and Risk Management with support on valuation and risk assessments. This includes calculation support as well as short-term developments around existing platform solutions. Develop and operationally support pricing and risk platform. Support digital roadmap by leveraging techniques from machine learning and deep learning to develop smart solutions for generating insights, supporting decision making and enhancing risk analytics. Understand front to end processes and work effectively across all levels of the organization to challenge, promote dialogue and drive cooperation.
University degree in natural sciences/engineering/mathematical finance or numerical related field. Proven track record of applying financial engineering/stochastic calculus/machine learning in a commercial setting. Excellent understanding of risk methods such as VaR, Credit-VaR etc desirable. Commercially oriented individual with ability to communicate complex solutions Strong team player with experience of working in an international and multicultural team Fluency in English
Proven track record of applying financial engineering/stochastic calculus/machine learning in a commercial setting University degree in natural sciences/engineering/mathematical finance or numerical related field Excellent understanding of risk methods such as VaR, Credit-VaR etc desirable experience of working in an international and multicultural team