Remote Sensing Data Scientist

About the position

As a Remote Sensing Data Scientist, you will deliver remote sensing insights to all pipelines and regions within Plant Breeding. You'll work collaboratively with interdisciplinary scientists, IT and engineering professionals across the organization to solve critical problems and answer important questions that drive key decisions for our business. You will foster new game changing ideas to produce innovative insights from imagery and sensor data with a focus on large scale geo-temporal analyses, computer vision and remote sensing, feature extraction from imagery and time series data, crafting complex model architectures using embeddings and ML/DL techniques. You'll be an individual contributor on a team with other Data Scientists.

Responsibilities

• Lead the efforts to create breakthroughs in precision plant breeding by exploring and developing the next generation of satellite enabled AI methodologies and models.
• Leverage recent advances in high resolution remote sensing and IoT sensors to develop models that predict novel phenotypes.
• Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline.
• Acquire, process, transform, and extract information from high resolution remote sensing imagery.
• Create innovative insights from imagery and sensor data with a focus on large scale geo-temporal analyses, computer vision and remote sensing, feature extraction from imagery and time series data, crafting complex model architectures using embeddings and ML/DL techniques.
• Independently perform computer programming, predictive modeling, statistical analysis, and experimental design using advanced mathematical models, machine learning algorithms, and strong business acumen to deliver insights, recommendations, and solutions.
• Build cross-functional relationships to partner with the business collaboratively and effectively network within the Data Science Community.
• Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact, and key performance indicators.
• Collaborate regularly, acquiring support and partnership from cross-functional scientific, engineering, and IT teams across the company.
• Present compelling, validated stories to all levels of organization, including peers, senior management, and internal customers to drive both strategic and operational changes in business.

Requirements

• Master's degree in relevant technical field with at least two years of experience OR PhD with strong educational preparation and some applied experience.
• Educational preparation or applied experience in at least one of the following areas: Geographical Information Systems, Machine Learning, Electrical/Industrial Engineering, Operation Research, Biostatistics, Computational Biology, Applied Mathematics, Generative AI, Computer Science and/or other related quantitative discipline.
• Intermediate proficiency in computational skills and level of experience building data models using Python, R or other programming packages, including CV/ML libraries and deep learning frameworks such as PyTorch.
• Basic understanding of software development best practices (Version Control, Code Documentation & Review).
• Proficiency using Python.
• Strong proficiency with geospatial and imagery data such as geophysical soil sensing, remote sensing, hyperspectral, multispectral imagery, open source geospatial technologies and large-scale cloud computing.
• Strong proficiency in predictive modeling to include comprehension of theory, modeling/identification strategies and limitations and pitfalls.
• Experience in the successful delivery of valuable analysis through the application of domain knowledge; evidence of ability to strong business acumen.
• Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise, and actionable manner to the extended team and small groups of key stakeholders.
• Experience with technologies such as: Diffusion, Latent Diffusion Models, Text-to-image synthesis, Image inpainting, Super-resolution, and Flow Matching.

Nice-to-haves

• Ph.D. with a degree related to Machine Learning, Computer Science, Electrical/Industrial Engineering, Operation Research, Biostatistics, Computational Biology, Applied Mathematics, Geographic Information Systems or other related quantitative disciplines.
• Strong proficiency with imagery, geospatial, or other high dimensional data such as hyperspectral imagery, multispectral imagery, remote sensing, geophysical soil sensing, open-source geospatial technologies, and large-scale cloud computing.

Benefits

• Health care
• Vision
• Dental
• Retirement
• PTO
• Sick leave