Data Scientists at Agrograph plays a vital role in developing, implementing, and deploying remote sensing-based machine learning algorithms for applied agricultural applications, such as crop identification, yield estimation, and sustainability variables determination. Our Data Science team mainly focuses on building field-scale predictive models for risk-based analysis of agricultural assets that can be useful to insurance companies, banks, and land-owners, etc. As a part of this team of highly skilled scientists and engineers, you will be expected to:

• Acquire, analyze, and model remote sensing and other EO data from satellites (Landsat, Sentinel, MODIS, Planet, etc) or national databases such as USDA NASS
• Create suitable time-series or machine learning based models on Google earth engine and/or python for prediction or estimation of agricultural metrics such as crop type, tillage, planting date, etc
• Clean and explore large geospatial and remote sensing datasets to ensure quality requirements and work closely with the team to resolve potential issues
• Conduct spatio-temporal analysis of remote sensing metrics such as NDVI and EVI and derive meaningful and scientific inferences of agricultural trends
• Collaborate with members of the science and engineering team to create efficient pipelines to meet necessary deadlines
• Conduct extensive research and literature study to create, modify, or optimize remote sensing-based machine learning models for different agricultural and Earth science applications
• Generate clean, robust, and well-documented code that adhere to our standards and best practices

Requirements:

• Familiarity with satellite or airborne remote sensing and its applications (eg., land cover-land use change mapping)
• Some experience working with multispectral or hyperspectral image data from satellites such as Landsat, Sentinel, MODIS, Planet, etc
• Knowledge of important remote sensing metrics such as NDVI, EVI, SAVI, etc
• Demonstrated experience (1-5 years) in deploying data science, machine learning, and/or statistical methods to solve real-world problems
• Fluency in Python or SQL for research, analysis, and/or modeling & validation
• Familiarity with Google Earth Engine
• MS/PhD in Geography, Atmospheric Sciences, Data Science, Computer Science, Physics, Statistics, Economics, Engineering, or a closely related discipline

Nice to haves:

• Strong foundation and experience in applying satellite EO data to solve problems pertaining to Earth Sciences, agriculture, and/or ecology
• Demonstrated experience in creating classification/regression machine learning models (logistic regression, random forests, gradient boosting, etc) using remote sensing, geospatial, and/or ground data to solve real-world problems
• Experience working with agricultural data from USDA (such as Cropland Data Layer) or other agencies
• Experience in working with spatial and time-series data and methods to observe, estimate, and predict land cover change and/or crop pattern
• Understanding of agricultural practices and/or knowledge of atmospheric sciences
• Experience working with version control such as git
• Demonstrated experience working with cloud platforms such as Google Cloud

Benefits:

• Contribution to Simple IRA retirement package with 3% matching
• Flexible schedule, Remote Position

Job Type: Full-time

Pay: $90,000.00 - $125,000.00 per year

Benefits:

• 401(k)
• 401(k) matching
• Flexible schedule
• Paid time off
• Parental leave
• Professional development assistance

Schedule:

• Monday to Friday

Supplemental pay types:

• Bonus pay

Work Location: Remote

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