About this role
Translating dense temporal stacks of satellite imagery into validated markers for agriculture, land management, and climate requires advanced machine learning expertise at Planet. The incoming data scientist will develop algorithms that extract insights from multi-sensor Earth observation data, directly shaping the Area Monitoring System delivered to European paying agencies. Responsibilities span from initial research exploration to maintaining production codebases, building embeddings for land-cover change detection, and integrating AI agents into everyday workflows.
Operating within a hybrid schedule requiring three days per week in the Graz, Ljubljana, or Haarlem office, the team collaborates closely to scale models and publish technical findings. Successful candidates bring strong Python programming capabilities, a firm grasp of machine learning validation principles, and readiness to adapt based on direct customer feedback.
What you will do
- Develop and improve algorithms and machine learning models that extract insights from satellite imagery time series.
- Take research topics from initial exploration through to production deployment.
- Own marker results for production Area Monitoring System deliveries across several European Union countries by running, reviewing, and confirming quality.
- Explore and build embeddings-based insight extraction in new areas beyond the Common Agricultural Policy.
- Use AI agents in daily work and help the team improve related workflows.
- Co-own the markers codebase alongside other team members and collaborate to build solutions on existing data building blocks.
- Document and organize work for transparency, producing internal research reports, public blog posts, and client reports.
What you need
- Hold at least four years of relevant work experience in a technical or analytical domain.
- Possess a bachelor's degree or higher in computer science, data science, or another STEM field.
- Demonstrate an understanding of machine learning principles, including model validation and performance evaluation techniques.
- Show proficiency in Python programming with the ability to write maintainable and well-documented code.
- Bring experience working with AI agents and modern machine learning tools.
- Utilize version control with Git and feel comfortable working in a shared codebase.
- Possess a working knowledge of the geospatial domain alongside professional working proficiency in English.
Nice to have
- Experience working with embeddings or other learned representations of imagery
- Remote sensing expertise, particularly with satellite time series
- Experience processing radar data such as Sentinel-1
- Familiarity with the agricultural domain or the European Union Common Agricultural Policy and area-based payment schemes
- Experience with cloud environments and distributed computing
Benefits
- 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 and volunteering opportunities
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
This is a full-time, hybrid role that requires working from the Graz, Ljubljana, or Haarlem office three days per week. Planet also operates with a global workforce with employees working remotely worldwide.
What is the compensation range for this position?
The expected starting gross salary range for Austria is €54.100 to €67.600 EUR. Individual placement is determined objectively based on skills, qualifications, and professional experience.
What kind of projects will I work on?
You will develop high-quality, validated markers that extract insights from dense temporal stacks of satellite imagery across agriculture, land management, and climate. Your work forms the core of the Area Monitoring System delivered to Common Agricultural Policy paying agencies across Europe.
What qualifications are required to apply?
Candidates need four or more years of relevant work experience and a bachelor's degree or higher in computer science, data science, or another STEM field. The role also requires proficiency in Python, machine learning validation knowledge, and working familiarity with geospatial domains.