Welcome to Planet. We believe in using space to help life on Earth.
Planet designs, builds, and operates the largest constellation of imaging satellites in history. This constellation delivers an unprecedented dataset of empirical information via a revolutionary cloud-based platform to authoritative figures in commercial, environmental, and humanitarian sectors. We are both a space company and data company all rolled into one.
Customers and users across the globe use Planet's data to develop new technologies, drive revenue, power research, and solve our world’s toughest obstacles.
As we control every component of hardware design, manufacturing, data processing, and software engineering, our office is a truly inspiring mix of experts from a variety of domains.
We have a people-centric approach toward culture and community and we strive to iterate in a way that puts our team members first and prepares our company for growth. Join Planet and be a part of our mission to change the way people see the world.
Planet is a global company with employees working remotely world wide and joining us from offices in San Francisco, Washington DC, Germany, and The Netherlands.
About the Role:
We are looking for a passionate data scientist to join the Forest Ecosystems engineering team. We are on a mission to map, measure, and monitor the world’s forests using high resolution satellite imagery. Our team works with multi-sensor earth observations data, developing workflows that integrate measurements from Planet’s unique constellation with public data sources, including multispectral, LiDAR, and SAR data. We are developing object detection models to identify and count individual trees in high resolution imagery. This work will span spatial and organismal scales, counting both recently planted saplings and mature standing trees from sites around the world. You will contribute by developing consistent, scalable, and science-based instance segmentation methods using sub-meter satellite imagery. As a member of this team, you will have the opportunity to work with an experienced team of scientists and engineers, experiment with deploying your models at scale, and iterate with feedback from users and stakeholders. We are a small and growing team, with a highly collaborative culture, distributed remotely across USA and Canada.
Impact You’ll Own:
- Develop novel methods to count and delineate trees in satellite imagery
- Collaborate with the team to iteratively build and test our suite of forest-related data products with end users, who are often focused on conservation and restoration
- Implement models as efficient python code that can be deployed at scale
- Document and organize your work to be transparent and repeatable
- Write external documentation, white papers and occasionally research papers
What You Bring:
- PhD in forest ecology, environmental science, forestry or related
- Solid understanding of machine learning or image analysis
- Research or professional experience deriving tree attributes from sensor data
- Fluency with geospatial technologies in python (e.g. GDAL, rasterio, shapely, etc)
- Proficiency with Python in a Linux environment
- Experience with version control and Git
- Excellent technical communication and documentation skills
What Makes You Stand Out:
- Experience training and deploying deep learning models
- Experience with cloud compute and storage
Benefits While Working at Planet:
- Comprehensive Health Plan
- Wellness program and onsite massages in specific offices
- Flexible Time Off
- Recognition Programs
- Commuter Benefits
- Learning and Tuition Reimbursement
- Parental Leave
- Offsites and Happy Hours
- Volunteering Benefits
Why we care so much about Belonging.
We’re dedicated to helping the whole Planet, and to do that we must strive to represent all of it within each of our offices and on all of our teams. That’s why Planet is guided by an ultimate north star of Belonging, dreaming big as we approach our ongoing work with diversity, equity and inclusion. If this job intrigues you, but you’re thinking you might not have all the qualifications, please... do apply! At Planet, we are looking for well-rounded people from around the world who can contribute to more ways than just what is listed in this job description. We don’t just fill positions, we aspire to fulfill people’s careers, most excited about folks who are motivated by our underlying humanitarian efforts. We are a few orbits around the sun before we get to where we want to be, so we hope you’re excited to come along for the ride.
EEO statement:
Planet is committed to building a community where everyone belongs and we invite people from all backgrounds to apply. Planet is an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws.
Accommodations:
Planet is an inclusive community and we know that everyone has their own needs. If you have a disability or special need that requires accommodation during the interview process, please call Planet's front office at 669-214-9404 or contact your recruiter with your request. Your message will be confidential and we will be happy to assist you.
Privacy Policy: By clicking Apply Now at the top of this job posting, I acknowledge that I have read the Planet Data Privacy Notice for California Staff Members and Applicants, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
Privacy Policy (European Applicants): By clicking Apply Now at the top of this job posting, I acknowledge that I have read the Candidate Privacy Notice GDPR Planet Labs Europe, and hereby consent to the collection, processing, use, and storage of my personal information as described therein.
