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# Data Scientist

- **Company:** Tibber
- **Location:** Berlin, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Software Deployment, Backend, Performance Monitor
- **Published:** September 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9637333d7a247721

## About the Role

You are driven by impact and know how to turn strategy into execution. You're used to fast-paced environments and thrive when given clear goals and high accountability. We believe you'll thrive here if you:

 * Have built and deployed forecasting or time-series models in production - not just in notebooks, and can speak to how those models held up in practice, Are curious about the energy trading domain - and motivated to understand the market mechanics behind the models, not just the math
*
Are a team player - you learn from those around you and share what you know freely
*
Are excited by AI tools - and how they are changing the way data scientists work and actively look for ways to build smarter, faster workflows with them.

We see it as a plus bust not a must if you also bring: experience in energy, power markets, or demand forecasting in regulated markets.

️ ️The Tibber Mindset

## Description

As a Data Scientist, you'll play a key role in keeping Tibber's energy trading function one step ahead. You'll build and continuously improve a whole portfolio of models: the forecasts our traders lean on when they buy power, and the models that decide how our virtual power plant puts thousands of home batteries and electric cars to work. You will have a direct, measurable impact on our trading outcomes.

What you'll do

Build and improve day-ahead energy consumption forecasting models that the trading team uses to make power purchase decisions.

 * Own the full model lifecycle - problem framing, data sourcing, model development, production deployment, and ongoing performance monitoring.
*
Take models from exploratory notebooks to reliable, production-ready Python systems built for live trading conditions.
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Collaborate with backend engineers to bridge model outputs into production infrastructure.
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Diagnose and respond to operational issues - missing data inputs, model drift, or unexpected forecast failures, without waiting to be asked.
*
Continuously raise the accuracy and reliability of our established forecasting systems with direct impact on trading outcomes.