Student data scientist for climate-tech startup

The role

We are looking for an ambitious and driven data science colleague. You will work closely with the core data and modelling team. This is a student position with real responsibility. We expect someone with relevant, hands-on experience who can contribute from day one. The focus is on applying modern data analytics approaches to our modelling work, which is centered around geospatial data workflows.

What you will do

• Contribute to model development and prototyping.

• Apply modern ML and AI methods and evaluate model performance.

• Produce clear visualisations and summaries for the team and pilot users.

• Work collaboratively with the modelling team and take ownership of defined tasks.

What we are looking for

• Currently enrolled in a BSc or MSc programme in computer science, data science, remote sensing, statistics or a related field.

• Relevant, hands-on experience building and training models in Python, for example through course projects, internships, research or work.

• Familiarity with modern data science approaches, such as deep learning, probabilistic methods, time-series techniques and contemporary architectures.

• Ability to prototype, evaluate models and explain model behaviour to non-technical stakeholders.

• Good coding practices and experience with python-based tools (notebooks and libraries such as pandas and rasterio).

• Based in or near Copenhagen and available for regular in-person collaboration.

• Nice to have: Experience with Earth Observation or geospatial data, and prior exposure to topics such as MCP and geo-spatial-temporal databases.

Why join Envira

• Real responsibility on modelling work that supports pilots and customers.

• Close mentorship from senior colleagues.

• Hands-on experience in an applied, impactful setting.

• Flexible hours to fit your studies and a collaborative office environment.

How to apply

Apply on The Hub with: CV, grade transcripts, a short motivation (one page max) and links or attachments showcasing relevant work (GitHub, notebooks, project reports). We prioritise demonstrated experience and practical results. If you have built working models or prototypes, we want to hear from you.