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Data Specialist & Project Manager - Golf and Turf Division
Base pay range
$60,000.00/yr - $75,000.00/yr
Ecorobotix Inc. creates innovative robotic solutions that reduce the negative ecological impact of modern agriculture. We have developed an AI-based ultra-high precision spot spraying technology that drastically reduces the amount of chemicals used in crop fields while keeping costs competitive. The technology is currently deployed on our tractor-towed ARA machine. By joining our talented and dynamic team, you will contribute to a more sustainable, environment-friendly agriculture in an innovative scale-up at the cutting edge of technology. We are a revolutionary organization with a great work environment that supports creativity through self-motivation.
General objective of the role
Support the development of Ecorobotix’s turfgrass care through data preparation and quality control of turf imagery and environmental data for AI model training and validation. This role will be responsible for preparing complex turfgrass datasets, scoring datasets for data preparation difficulty, performing quality control checks on pre-labeled imagery, as well as creating and managing documentation for the labeling of turfgrass imagery by external annotators. The Turf Data Specialist & Project Manager will become subject matter experts for various turfgrass models and will come in with expertise in turfgrass management systems (e.g. sod production, golf course management, athletic field management, lawn care). Quality control and data analysis will take 50% of their time, and the other 50% will be managing current and future model projects.
Reporting lines and interactions
Reports to the Turf Data Team Manager, Turf Division. The Turf Data Specialist & Project Manager will collaborate with the Turf Data Collection team members in the United States and Europe as well as with the AI and Crop Vision Team to ensure data quality standards and data preparation quantity goals. Externally, this role will interact with third-party data preparers to achieve data quality standards.
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