• Design, prepare data and conduct analysis and delivery of RWE analyses for biopharma clients. • Generate insights from complex real-world endpoints using extensive coding, demonstrating deep comprehension of Natera clinical and molecular data structures and complexity. • Build technical standards by implementing advanced methods in survival analysis, machine learning and predictive modeling. • Integrate practical adoption of Natera tools including: LLMs and agentic tools into your own daily workflow. • Own the communication of high impact/value results to internal stakeholders and external partners. • Collaborate with internal product, oncology, and clinical abstraction, medical, scientific, business development and consortia teams to continually enhance Natera data quality, products, and analytical best practices. • Proactively identify gaps in current products and ensure that customer feedback is collected and integrated into the Natera product road map. • Maintain deep expertise in oncology clinical guidelines (e.g., NCCN) and emerging RWE methodologies.

• Strategy Execution: Lead on data strategy execution, leading by example in a hands-on capacity • Data Foundations: Design and create the single source of truth for critical business entities and implement processes to ensure the business builds on solid data foundations • Data Products: Develop data products that allow for controlled, self-service data access • Leadership: Lead and mentor the data team, fostering a culture of high performance and innovation • Governance & Quality: Own data quality and governance in an FCA-regulated environment, ensuring accurate audit trails • Cross-Functional Collaboration: Work closely with the Salesforce team to own Salesforce data models and ensure holistic governance • Technical Alignment: Work with the VP of Engineering to ensure alignment with the wider technical strategy and business objectives.

Role Description We are seeking a distinguished Visiting Staff Scientist to join our AI Research (AIR) team for a one-year sabbatical residency. In this role, you will play a pivotal part in our mission to create a “Queryable Earth” by leading the development of Planet’s proprietary geospatial foundation models (GFMs). - Lead the research and development of a foundation model specifically trained on Planet imagery, incorporating the time-axis to create high-cadence time-series embeddings. - Systematically evaluate and compare existing GFMs (e.g., TerraMind, Prithvi, Clay) against PlanetScope data to assess performance, computational cost, and transferability. - Design embeddings and workflows optimized for detecting short-lived, high-impact events such as floods, rapid surface-water expansion, and fire. - Explore the synergy between PlanetScope, Sentinel-1 SAR, and other commercial SAR data to ensure robust time-series analysis even under cloud cover. - Use embeddings to design active learning workflows that prioritize labeling and reduce the annotation burden for time-sensitive mapping tasks. - Publish findings in top-tier journals and present at conferences (e.g., IGARSS, CVPR), highlighting PlanetScope’s unique value in the foundation model ecosystem. - Oversee the technical direction of a dedicated postdoc and collaborate with Planet’s research scientists to transition prototypes into operational products. Qualifications - PhD and current Faculty/Professor status in Geospatial Analytics, Computer Science, Remote Sensing, or a related field. - 12+ years of experience in remote sensing and satellite image analysis, with a proven track record in building AI-based models for environmental change (e.g., flood-extent, water dynamics). - Extensive experience with foundation models, contrastive learning (CLIP-like models), and multi-model vision-language models (MMVLMs). - Proficiency in multi-sensor integration (Landsat, Sentinel-2, PlanetScope, Sentinel-1) and high-resolution mapping at varying scales (3m, 10m, 30m). - Expert-level Python skills and experience with the scientific stack (xarray, Dask, NumPy, Rasterio, GeoPandas) and deep learning frameworks. - Experience building automated pipelines for preprocessing and labeling planetary-scale datasets. - A history of leading research labs and a desire to work in a fast-paced, industrial R&D environment. Requirements - Extensive experience specifically in flood damage quantification and methane-related water dynamics. - History of leading NASA-funded or similar high-impact geospatial research projects. - Direct experience fine-tuning or modifying specific GFM architectures like TerraMind or Prithvi. - A mix of deep academic rigor and the ability to prototype rapid-change monitoring tools for operational readiness. Benefits - Comprehensive Medical, Dental, and Vision plans - Health Savings Account (HSA) with a company contribution - Generous Paid Time Off in addition to holidays and company-wide days off - 16 Weeks of Paid Parental Leave - Wellness Program and Employee Assistance Program (EAP) - Home Office Reimbursement - Monthly Phone and Internet Reimbursement - Tuition Reimbursement and access to LinkedIn Learning - Equity - Commuter Benefits (if local to an office) - Volunteering Paid Time Off

Role Description We are seeking a highly motivated Visiting Scientist (Postdoctoral Researcher) to join our AI Research (AIR) team for a one-year residency. In this role, you will work directly with Dr. Mirela Tulbure during her sabbatical at Planet to develop our proprietary geospatial foundation models (GFMs). While Planet has historically leveraged external models, we are now focused on building in-house models specifically trained on our unique imagery. As a postdoctoral researcher, you will be the primary technical engine behind creating temporally dense embeddings that capture the dynamic and ephemeral nature of our planet—such as rapid flooding and disaster impacts. You will collaborate with "Planeteers" across data pipelines and analytics to bridge the gap between academic research and operational AI/ML solutions. Impact You’ll Own - GFM Implementation: Contribute to the design and training of a foundation model specifically optimized for Planet imagery, focusing on the integration of time-series data. - Technical Benchmarking: Execute the systematic evaluation of existing GFM architectures (e.g., TerraMind, Prithvi, Clay) against PlanetScope data to identify performance bottlenecks and transferability. - Prototype Development: Build and test workflows for detecting short-lived events, such as floods and fires, using high-cadence embeddings. - Multi-Sensor Data Fusion: Develop methods to integrate PlanetScope with Sentinel-1 SAR and other commercial datasets to maintain time-series continuity under cloud cover. - Research to Production: Work closely with Planet’s research scientists to transition experimental prototypes into scalable, operational products. - Scholarly Contribution: Co-author findings for publication in top-tier journals and present research at leading conferences like IGARSS or CVPR. Qualifications - A recently completed PhD in Geospatial Analytics, Computer Science, Remote Sensing, or a related field. - Demonstrated experience in building AI-based models for environmental change or satellite image analysis. - Hands-on experience with foundation models, contrastive learning, and deep learning frameworks (PyTorch/TensorFlow). - Expert-level Python skills and proficiency with the geospatial scientific stack (e.g., xarray, Dask, Rasterio, GeoPandas). - Experience building automated pipelines for preprocessing and labeling planetary-scale datasets. - Experience working within a research lab environment and a strong desire to apply academic rigor to industry challenges. What Makes You Stand Out - Prior research in flood-extent mapping, water dynamics, or disaster response. - Direct experience fine-tuning or modifying specific GFM architectures like TerraMind, Prithvi, or Clay. - Proven ability to work with a variety of sensors including PlanetScope, Landsat, and Sentinel-1/2. - A history of developing "human-in-the-loop" workflows or active learning strategies for labeling time-sensitive data. Application Deadline August 12, 2026 by 11:59p / 23:59 CET (Central European Time) Benefits - Comprehensive Medical, Dental, and Vision plans - Health Savings Account (HSA) with a company contribution - Generous Paid Time Off in addition to holidays and company-wide days off - 16 Weeks of Paid Parental Leave - Wellness Program and Employee Assistance Program (EAP) - Home Office Reimbursement - Monthly Phone and Internet Reimbursement - Tuition Reimbursement and access to LinkedIn Learning - Equity - Commuter Benefits (if local to an office) - Volunteering Paid Time Off