Senior Data Scientist: District Energy System Optimizer

DANFOSS A/S; Nordborg; On-site; DKK 750,000 - 950,000; Full time

Job summary

DANFOSS A/S in Copenhagen or Kolding, Denmark, is seeking a Senior Data Scientist to deepen the modelling and optimization core of our Utility team. You will apply mathematical modelling, programming skills and optimization theory to ensure our products deliver maximal value to customers.

The role focuses on system-level optimization across production, network and demand, integrating physics-based models with data-driven methods and MILP for grid-scale district energy.

Qualifications

• Master’s or PhD in a quantitative field preferred.
• Experience applying data modelling to complex physical or industrial systems.
• Excellent command of Python as main programming tool.
• Solid knowledge of district energy and thermo-hydraulic modelling.
• Familiarity with numerical methods for dynamic physical systems.

Responsibilities

• Develop and extend thermo-hydraulic simulation tools for district energy networks using physical modelling and machine learning.
• Calibrate and validate models against live network measurements.
• Advance production optimization using MILP and related techniques.
• Connect production, network and demand into system-level optimization.
• Build data pipelines with the data engineering platform team.
• Communicate solutions to customers and stakeholders.

Skills

Python

Machine learning

Data modelling

Optimization

Fluency in English

Communication

Education

Master’s degree in quantitative field

PhD is a benefit

Tools

MILP solvers

C++

C#

Job description

DANFOSS A/S in Copenhagen or Kolding, Denmark, is seeking a Senior Data Scientist to deepen the modelling and optimization core of our Utility team. You will apply mathematical modelling, programming skills and optimization theory to ensure our products deliver maximal value to customers.

The role focuses on system-level optimization across production, network and demand, integrating physics-based models with data-driven methods and MILP for grid-scale district energy.