Data Science Working Student in a Greentech Startup - Berlin, Deutschland - Green Fusion GmbH
Beschreibung
Digitalization and energy transition in one sentence? Yes, at Green Fusion With our software for optimized control of heating systems in the housing industry, we are counteracting climate change.
By digitizing and automating the control processes, we can not only reduce emissions, but also energy consumption, and thus actively accelerate the energy transition.
While we are still considered pioneers today, with you we can soon dominate the market First in the DACH region, and next all of Europe.
Aufgaben
- Develop datadriven models to predict the operation of energy systems (e.g. heat pumps, solar panels, battery storages) under various different conditions.
- Create optimal energy plans for a given building.
- Experience in doing machine learning or statistics of some description in Python or other relevant programming language.
- Some experience in one of Keras, Tensorflow, SciKit Learn or another machine learning library and solid Pandas knowledge.
- Interest in the renewable energy area.
Nice to haves:
- Experience reinforcement learning.
- Experience in forecasting models.
- SQL or GraphQL experience.
- Knowledge of energy systems.
Benefits:
Green Fusion is a company like a few others out there, and you can expect this role to be both challenging and rewarding.
A cool office in the middle of Berlin - with a coffee machine, fresh fruits and everything you would expect from the start-up life & yes, of course also the Urban Sports Club or Become1 membership is included.
You will learn a lot - whether through the challenges in the job, our open feedback culture or further training - the opportunity to learn is everywhere.
Direct impact through your job - with us, you can actively promote the energy transition and contribute to the fight against climate change every day.
We value our team - that's why regular team events, but also a good salary and the chance to receive virtual shares are very important to us.
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