Founded in 2016, Heimdall Power is on a mission to make the world's power grids smarter and more resilient. Today, more than 50 utilities across 20 countries rely on us to get more out of their grids — boosting performance, preventing outages, protecting critical infrastructure, and staying ahead of regulations.

At the heart of what we do is the award-winning Neuron™, our hardware sensor device that mounts directly on power lines. The data it generates is only as valuable as the models that interpret it — and that's where you come in.

The role

We are looking for a Senior Data Scientist to join our Oslo team. You'll work at the intersection of physics and machine learning, developing models that turn raw grid data into actionable intelligence for utility operators worldwide. This is a hands-on role with real impact, where the models you build shape decisions on critical infrastructure.

Key responsibilities

• Translate physical phenomena, such as thermal behaviour or mechanical stress into robust, physics-informed models for applications including Dynamic Line Rating (DLR) and asset health monitoring.

• Design, develop, and continuously improve predictive and physics-informed models for power grid applications such as Dynamic Line Rating (DLR) and asset health monitoring.

• Collaborate closely with domain experts, engineers, and product teams to translate physical phenomena into robust, production-ready models.

• Work on building, training, and evaluating models at scale.

• Contribute to the full data science stack where relevant — including data pipelines, model hosting, and deployment infrastructure.

• Drive scientific rigour and best practices across the data science team.

• Communicate findings and model performance clearly to both technical and non-technical stakeholders.

Qualifications and skills

• A strong foundation in physics — ideally through a degree or professional background in electrical, mechanical, or thermal engineering.

• Minimum 3 years of experience in data science or a closely related field.

• Solid experience with machine learning, statistical modelling, and model development workflows applied to real-world physical or engineering problems.

• Demonstrated ability to translate physical domain knowledge into data-driven models and solutions.

• Proficiency in Python and experience working with large-scale cloud-based data platforms (Databricks experience is a strong advantage).