Energy Forecasting Data Scientist - Production-ready Models

Danfoss GmbH; København; On-site; DKK 750,000 - 950,000; Full time

Job summary

Danfoss is seeking a Data Scientist to strengthen the forecasting capability in our Utility team based in Copenhagen or Kolding, Denmark. You will develop models to run daily on live energy networks and bring them to production, measuring energy-economic value and decarbonisation impact.

You'll work on energy demand forecasting, weather and condition forecasting, and hybrid physics-plus-data modelling, collaborating with experienced data scientists to scale solutions across multiple district

Qualifications

• Master’s degree or higher in a relevant field (math, ML, statistics, physics).
• PhD is a benefit, but not required.
• Proficient Python with statistics and ML background.
• Experience with forecasting/time-series in production environments.
• Familiarity with energy systems or electricity markets is a plus.
• Experience with modern data pipelines and cloud platforms.
• Knowledge of C# and C++ is appreciated.

Responsibilities

• Build and improve energy demand forecasting models for utility networks.
• Develop local weather/condition forecasts to outperform general models.
• Ensure forecast robustness at scale with self-adaptive methods.
• Blend physics-based models with data-driven approaches.
• Create and maintain data pipelines with the engineering platform team.

Skills

Python

Time-series forecasting

Statistical modeling

Machine learning

English fluent

Production forecasting

Code in production

Education

Master’s degree or higher

PhD degree is beneficial

Tools

C# and C++

Cloud-based data platforms

Data pipelines

Job description

Danfoss is seeking a Data Scientist to strengthen the forecasting capability in our Utility team based in Copenhagen or Kolding, Denmark. You will develop models to run daily on live energy networks and bring them to production, measuring energy-economic value and decarbonisation impact.

You'll work on energy demand forecasting, weather and condition forecasting, and hybrid physics-plus-data modelling, collaborating with experienced data scientists to scale solutions across multiple district