Machine Learning / Data Engineer (m/w/d) gesucht für Energy Forecasting

As a Machine Learning and Data Engineer, you’ll be joining our team in the founding phase and working directly on developing forecasting models for the energy market. Your focus will be on time series analysis to enable precise predictions. You’ll have a lot of freedom to contribute your ideas, and your opinion matters. You’ll develop models that are continuously improved with new technologies and data, contributing significantly to shaping the direction of our company.

Your Responsibilities

Develop and implement machine learning models with a focus on time series analysis for the energy sector.

Analyze and prepare consumption and production data and integrate additional relevant data sources (e.g., weather data).

Create forecasts and analytical methods to support our clients in efficient energy use.

Test and optimize models based on current research and the specific needs of our partners.

Compare with classical forecasting models (ARIMA and variations).

Actively participate in shaping and advancing our technology roadmap and product strategy.

• Machine Learing
• Data analysis
• Data science
• Data analytics & BI

Du bist gut mit...

• Zahlen, Daten und Logik

Some to several years of experience in developing machine learning models, ideally in time series analysis.

Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).

Experience with energy or smart grid data is a big plus.

Interest in contributing to startup development and having a founder’s mindset.

Willingness to continually learn new technologies and drive the development of our models forward.

Equity in the company: Become an integral part of our founding team and earn equity in the company.

Innovative projects: Work on highly relevant projects with the potential to actively promote the energy transition.

Development opportunities: Constant growth and a chance to bring your ideas to life.

Impact: Through our work, we contribute to CO₂ reduction and promote sustainable practices.