Working Student Forecasting Natural Gas & LNG Markets (f/m/d)
As a global company, we generate, trade, and market energy on a large scale to business clients. In addition, we source, store, transport, and deliver various forms of energy. We are constantly on the lookout for talented individuals who will join us in developing simple solutions to the complex energy challenges of our customers. At Uniper, we approach this with great passion and teamwork, and from our perspective, we do it more effectively and seamlessly than others.
Diversity is at the heart of what we do, and we welcome applications from everyone - regardless of gender, disability, background, nationality, ethnicity, religion, belief, age, sexual orientation, or identity. #EveryoneIsWelcome
Apply and become part of our team. We look forward to get to know you.
- Support the development of Market Analysis' inhouse market forecasting models built on daily time series data
- Research and analyze market data to understand different aspects of the market (e.g. supply, demand, pipelines, storage etc.) and reveal areas for model improvement
- Process and structure market data for an implementation in the natural gas & LNG market models
- Back-test and calibrate new model components to refine assumptions and model logic
- Support the automation process and integrate new data and model components into the model's daily run schedule
- Support the creation of end-user reports that show market insights and improved model results to stakeholders at Uniper
- Quantitative background and degree (e.g. mathematics, engineering, economics) with excellent interim results
- Advanced Python skills with knowledge of relevant packages for statistical models and machine learning required
- Excellent problem-solving skills and strong analytical capabilities, strong interest in using quantitative methods to answer market questions
- Knowledge of energy markets, especially natural gas and LNG, would be advantageous
- Proficient knowledge of the English language
- Team player with a high degree of self-motivation
- Ability to present and describe findings and results comprehensibly