ML-Driven Quantitative Trading Analyst

Danske Commodities; Aarhus kommune; On-site; DKK 900,000 - 1,200,000; Full time

Benefits offered by this job

Flexible work life

Home setup covered

Bonus scheme

Job summary

Danske Commodities seeks a data-science/data-engineering professional to design and deploy machine-learning driven trading strategies for US short-term power markets. You will transform high-dimensional data into signals, collaborate with US traders, and own production-ready ML solutions in a fast-moving energy context.

You will work with a team applying quantitative methods to European electricity trading, bringing research into practical, measurable value while continuously improving models

Qualifications

• Hold an MSc or PhD in data science, computer science, mathematics, physics, or a related field.
• Advanced Python developer with production-quality code experience.
• Extensive experience with ML techniques and frameworks (e.g. PyTorch, Scikit-learn).
• Stay current with ML and DL advances and translate research into practical value.

Responsibilities

• Design, develop, and deploy ML-driven trading strategies using Python and modern ML frameworks.
• Transform large-scale datasets into actionable trading signals and predictive models.
• Continuously improve models, infrastructure, and research processes for a competitive edge.
• Collaborate with US traders and analysts to translate market insights into scalable solutions.
• Own bringing new ideas into production and stay at the forefront of ML and quantitative finance.

Skills

Python

ML frameworks

Production code

Data analysis

Education

MSc or PhD in data science or related

Tools

PyTorch

Scikit-learn

Job description

Danske Commodities seeks a data-science/data-engineering professional to design and deploy machine-learning driven trading strategies for US short-term power markets. You will transform high-dimensional data into signals, collaborate with US traders, and own production-ready ML solutions in a fast-moving energy context.

You will work with a team applying quantitative methods to European electricity trading, bringing research into practical, measurable value while continuously improving models