Since our inception in 2008, Alipes has been a trailblazer in automating financial markets, continuously growing and diversifying into new business areas through cutting-edge technology. In 2022 we entered the Energy sector, focusing on short-term physical power trading using machine learning to interpret fundamental and market data for our strategies. We are committed to supporting liquid power markets and the free flow of energy—key enablers in transitioning from fossil fuels to renewable sources—promoting a sustainable future through innovation.

About the Energy Quant Team:
Our Energy trading is powered by a small, elite team of 7 PhD-level quants. Currently focused on short-term physical power, we blend advanced mathematics, computer science, and market insight to execute automated strategies. As we explore new opportunities in energy markets, we rely on a culture of collaboration, intellectual rigor and a passion for computational excellence, where every member’s work shapes our next steps.

The Role:
We are seeking a Quantitative Researcher to join our team, enhancing our capabilities in Energy trading. You will develop predictive models and construct robust datasets to drive innovative trading strategies. Leveraging your skills in predictive analytics and computational techniques, combined with experience in power or gas markets or forecasting energy fundamentals, you’ll work autonomously on high-impact projects while collaborating with our close-knit group to advance our mission.

Key Responsibilities:

• Design and implement advanced predictive models to forecast energy market trends and inform trading strategies.

• Build, curate, and maintain high-quality datasets by integrating diverse energy market data sources, including fundamental and real-time market inputs.

• Conduct in-depth research and analysis of energy market dynamics to support model development.

• Develop, test, and deploy production-level code for trading systems, predictive tools, and data pipelines.

• Optimize algorithms, models, and datasets for performance, accuracy, and scalability.

• Collaborate with the team to share insights and refine strategies, while owning your individual projects.

About You:

• Advanced degree (PhD preferred) in a quantitative field (e.g., Mathematics, Physics, Computer Science, Engineering, or similar).

• Hands-on experience in power or gas markets, or in forecasting key energy fundamentals such as solar and wind power production or demand.

• Expertise in predictive modelling, with a track record of developing accurate forecasting tools for financial or energy markets.

• Strong skills in building and managing complex datasets, including data sourcing, cleaning, and structuring for analysis.

• Advanced programming skills in Python, C++, and/or C#, with a focus on clean, efficient, and reliable code.

• Solid background in computer science and software development, including data structures, algorithms, and system design.

• Passion for computers and technology, with a proactive approach to learning and problem-solving.

• Ability to work independently in a fast-paced environment while delivering high-quality results.

• Excellent analytical skills and a rigorous approach to modelling, research, and data validation.

• Experience with large-scale data analysis or machine learning applications in trading.

What we can Offer You:

• Flat hierarchy and high levels of trust and autonomy

• Exciting problems to solve

• Collaborative team made up of really smart and curious people

• Nice office in Nordhavn with a hybrid work model

• Pension, Health Insurance and 30 days of vacation

Practicalities:

To apply, please send us your CV and a cover letter telling us why you think this role is a great fit for you. If your profile looks like a good match, we will get in touch to coordinate an online technical test as a first step in the interview process.

Should you have questions about the role that you would like clarified before applying, you're welcome to send an email to quant_jobs@alipescapital.com. Please note that applications should not be sent to the email address but rather via the job link.