ML-Driven Quant Trader: Energy Markets & Automation

Danske Commodities; Aarhus; On-site; DKK 900,000 - 1,300,000; Full time

Benefits offered by this job

Growth plans and DC University courses

Harvard Learning access

Flexible work life: up to 2 work from

Pension and health insurance

Bonus scheme

Equinor international network

Office in Aarhus

Job summary

Danske Commodities is seeking a quantitative researcher to design, implement and maintain ML-driven trading strategies for the European electricity markets, using Python and modern ML frameworks.

You will join our Short-term Automated Trading team, developing and operating algorithmic strategies with ownership from research to deployment and close collaboration with traders and analysts across markets.

Qualifications

• MSc/PhD in data science, computer science, mathematics, physics or related field.
• Advanced Python developer with production-quality code experience.
• Extensive experience with ML techniques and frameworks (PyTorch, Scikit-learn).
• Keep up with latest ML/research and translate into practical value.

Responsibilities

• Design, develop, and deploy ML-driven trading strategies using Python and ML frameworks.
• Transform large-scale, high-dimensional data into actionable signals and models.
• Continuously improve models, infrastructure, and research processes for competitive edge.
• Collaborate with US traders and analysts to translate insights into scalable solutions.
• Own new ideas from research to production in ML and quantitative finance.

Skills

Python

Machine learning

Quantitative analysis

Education

MSc or PhD in data science/computer science/mathematics/physics or related field

Tools

PyTorch

Scikit-learn

Job description

Danske Commodities is seeking a quantitative researcher to design, implement and maintain ML-driven trading strategies for the European electricity markets, using Python and modern ML frameworks.

You will join our Short-term Automated Trading team, developing and operating algorithmic strategies with ownership from research to deployment and close collaboration with traders and analysts across markets.