Data Scientist
4 weeks ago
Data Scientist
Onsite in St. Louis, MO, no C2C, must be a US Citizen.
We would expect from you:
Experience: You have Ph.D. (or MS with 4+ years of post-MS experience) in one of the following areas: Remote Sensing, Image Processing, Computer Vision, Data Science.
Geospatial data: You have experience working with geo-spatial data (eg. satellite imagery, UAV etc). You have experience using open-source tools for spatial data analysis and visualization (eg. QGIS, GDAL). You are proficient in handling and processing different geospatial formats (eg. GeoJSON, SHP, GeoTIFF).
Geospatial Analytics and Machine Leaning: You have demonstrated understanding of machine learning/deep learning frameworks, statistical concepts, and geo-statistics and spatial data analysis. You have experience in geospatial image processing with a focus on orthorectification techniques of imageries.
Cloud Computing: You have familiarity with cloud computing platforms (e.g., AWS, Google Cloud Platform, Azure).
Programming: You are proficient in Python or other high level programming language with experience in using geospatial and machine learning libraries (eg., OpenCV, TensorFlow, PyTorch etc). You are also capable of writing clean code following coding best practices.
Soft skills: You have a high sense of ownership and are motivated to deliver valuable analysis. You have excellent communication skills and can clearly explain technical concepts to non-experts.
Description:
Position Overview
We are seeking a highly skilled and experienced Geospatial Data Scientist with a strong background in remote sensing and image processing to join our team. The ideal candidate will have a deep understanding of remote sensing principles, satellite imagery analysis, and advanced image processing techniques. You will use advanced analytics and collaborate with scientists from different backgrounds, IT and engineering professionals to deliver analytics solution to solve complex agriculture problems.
Key Responsibilities
You will leverage background in remote sensing, image processing, statistical, geo-statistical and machine learning models to analyze complex imagery data to develop an scalable image processing pipeline.
You will write model documentation to detail problem formulation, modeling approach, validation, data requirements and implementation steps.
You will follow data science best practices including peer review, code review, documentation, coding standards, and ensuring reproducibility.
Collaborate and Communicate
You will build cross-functional relationships to partner with the business stakeholders and collaborate with Bayer's Data Science community to co-develop innovative solutions.
You will communicate results to key stakeholders in a clear and compelling manner.
We would expect from you:
Experience: You have Ph.D. (or MS with 4+ years of post-MS experience) in one of the following areas: Remote Sensing, Image Processing, Computer Vision, Data Science.
Geospatial data: You have experience working with geo-spatial data (eg. satellite imagery, UAV etc). You have experience using open-source tools for spatial data analysis and visualization (eg. QGIS, GDAL). You are proficient in handling and processing different geospatial formats (eg. GeoJSON, SHP, GeoTIFF).
Geospatial Analytics and Machine Leaning: You have demonstrated understanding of machine learning/deep learning frameworks, statistical concepts, and geo-statistics and spatial data analysis. You have experience in geospatial image processing with a focus on orthorectification techniques of imageries.
Cloud Computing: You have familiarity with cloud computing platforms (e.g., AWS, Google Cloud Platform, Azure).
Programming: You are proficient in Python or other high level programming language with experience in using geospatial and machine learning libraries (eg., OpenCV, TensorFlow, PyTorch etc). You are also capable of writing clean code following coding best practices.
Soft skills: You have a high sense of ownership and are motivated to deliver valuable analysis. You have excellent communication skills and can clearly explain technical concepts to non-experts.
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