Quantitative Trader – Decision Systems

Om jobbet

At Qurrent we build the technology that enables a power grid driven by renewable energy. We believe that the energy system will change completely in the next decade and that the telemetry, software and algorithms that we develop will help solve the challenges that this transition brings.

By connecting, controlling and optimizing renewable power resources and battery storage, we support the power grid. This creates flexibility, increases sustainability and creates incentives for green investments.

We value innovation, automation, drive, and collaboration, offering a dynamic environment where your analytical skills and work will play an important role in Qurrent's future success. We always strive to be at the leading edge at what we do and are looking for someone that is excited to push the boundaries of systematic trading in energy markets.

The role
We are now looking for a Quantitative Trader to work on tuning our Decision Systems - the models that determine when, why, and how much we trade.

In this role, you will refine and advance the models that power our trading decisions. Your work will ensure we capture relevant market opportunities, manage risk intelligently, and continuously strengthen our systematic edge.

What you will do

• Monitor and analyze live trading algorithms, identifying risks, inefficiencies, and new opportunities
• Improve and extend statistical, machine learning, and structural forecasting models across multiple time horizons
• Design, execute, and evaluate rigorous experiments and backtests to validate and optimize performance
• Contribute to the ongoing development of our systematic trading framework and research infrastructure
• Build deep expertise in the markets and products we trade, translating domain insight into model improvements

What you bring

• Strong foundation in statistics, time-series analysis, and quantitative research
• Hands-on experience applying machine learning in financial or energy markets
• Deep understanding of backtesting pitfalls, overfitting, and non-stationary data
• Interest in software engineering, with a commitment to clean code and reproducible research workflows
• Performance-driven mindset combined with disciplined risk management
• Intellectual curiosity, analytical rigor, and the confidence to challenge assumptions