Challenges:
- You will be applying machine learning approaches for forecasting and visualizing data in Python to provide business insights for internal customers of the Deutsche Telekom group.
- You will be responsible for selecting the right modelling approaches specifically on signal data and data such as energy consumption, building temperatures, weather.
- You prepare the training & evaluation pipelines and apply state of the art Machine Learning approaches to improve forecasts for energy consumption, price developments and optimization of energy usage in buildings.
- Deploying final models to production, teaching the users how to use them and providing support/maintenance is another challenge in this job.
- You will cooperate with the business domain users and actively contribute to customer workshops, meetings, and calls so the ability to explain technical points to business colleagues is a crucial part of this job, as is a consultant’s mindset.
- Participation in team brainstorming sessions, code reviews, and pair programming sessions.
Skills:
- Experience of at least 6 years in software development and data analysis using Python programming language.
- Proven track record in Data Science projects in the last 4 years, preferably specialized on Time Series modeling and forecasting for signal data and energy data.
- Experience in a variety tools and technologies such as Google BigQuery, Scikit-learn, Keras, PyTorch, Scipy, Docker, Kubernetes, Flask or FastAPI, Jupyter notebooks is more than welcome.
- Experience in modeling energy consumption scenarios and systems in a simulation software such as Modelica, Dymola, Simulink completes your profile.
- BI dashboarding tools like MicroStrategy, Qlik, Tableau; knowledge of these is helpful.
- Flexible and responsive to changing work patterns and demands and preferably the candidate has already successfully applied agile methodology (e.g., Scrum)
- Analytical thinker that can develop working solutions quickly and you get things done.
- Fluent written and spoken English. German is nice to have, but not necessary.
