Data Scientist

Planet · Ljubljana, Slovenia · Data

Posted 2026-09-15

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About the Role:

We are looking for a Data Scientist to join our team in developing high-quality, validated markers that extract insights from dense temporal stacks of satellite imagery across agriculture, land management, and climate. The markers you build form the core of our Area Monitoring System (AMS) delivered to Common Agricultural Policy paying agencies across Europe, owned end-to-end from method to production code. You will collaborate closely with scientists and engineers to deploy models at scale, while expanding beyond compliance into land-cover change detection using embeddings and AI-first workflows.

Ideal candidates are adaptable, curious about AI agents, and eager to iterate directly based on customer feedback. As a member of this team, you will have the opportunity to work with multi-sensor Earth observation data to solve complex environmental and agricultural challenges.

This is a full-time, hybrid role which will require you to work from our Ljubljana office 3 days per week.

Impact You’ll Own:

Develop algorithms and machine learning models that extract insights from satellite imagery time series, and maintain and improve the markers we already run.

Take a research topic from exploration through to production.

Own marker results for production AMS deliveries across several EU countries — run them, review them, and confirm quality before they reach the client.

Explore and build embeddings-based insight extraction in new areas beyond CAP.

Use AI agents in your daily work, and help the team get better at it.

Co-own the markers codebase together with the rest of the team.

Collaborate with the team to iteratively build solutions on our platform and existing data building blocks, and inspire and enable our partners to extract insights using them.

Document and organize your work to be transparent and repeatable.

Write internal research reports, public blog posts, and reports for clients.

What You Bring:

4+ years of relevant work experience.

Bachelor's degree or higher in computer science, data science, or another STEM field.

Understanding of machine learning principles, including model validation and performance evaluation techniques.

Proficiency in Python programming, with the ability to write maintainable, well-documented code.

Experience working with AI agents and modern machine learning tools.

Experience with version control and Git, and comfort working in a shared codebase.

Working knowledge of the geospatial domain.

Ability to deliver projects on schedule and manage technical deliverables.

Problem-solving skills in technical or analytical domains.

Professional working proficiency in English, the language of the company.

What Makes You Stand Out:

Experience working with embeddings or other learned representations of imagery.

Remote sensing expertise, particularly with satellite time series.

Experience processing radar data (for example Sentinel-1).

Familiarity with the agricultural domain, or with the EU Common Agricultural Policy and area-based payment schemes.

Experience with cloud environments and distributed computing.

Application Deadline:

December 13, 2026 by 11:59p / 23:59 CET (Central European Time)

Benefits While Working at Planet:

These offerings are dependent on employment type and geographical location, based upon applicable law or company policy.

Paid time off including vacation, holidays and company-wide days off

Employee Wellness Program

Home Office Reimbursement

Monthly Phone and Internet Reimbursement

Tuition Reimbursement and access to LinkedIn Learning

Equity

Volunteering Paid Time Off

Compensation:

The expected starting gross salary range for this role is listed below. Individual placement within this range is determined objectively based on gender-neutral criteria such as your skills, qualifications, and professional experience. This position may also be eligible for discretionary bonuses, commission, and/or equity.

Slovenia Salary Range

€45.600—€57.000 EUR

San Francisco Fair Chance Ordinance

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