Agronomic Modeler (Corn)
About The Position
InnerPlant is seeking an Agronomic Modeler to refine and continuously improve the corn disease and pest models behind their commercial product, CropVoice. The CropVoice platform translates signals from living sensors to pinpoint stress well before visible symptoms, giving farmers and agronomists critical and timely insights and removing the guesswork behind key in-season decisions. The Agronomic Modeler will be accountable for refining the models behind CropVoice's corn recommendations and for making those models accurate, explainable, and reliable for the company, as well as farmers across the Corn Belt. They will work collaboratively with the InnerPlant team to improve models and explain resulting data and recommendations clearly.
Requirements
- Bachelor's or Master's degree in a STEM field, PhD preferred.
- At least 3 years of experience building and deploying relevant models, ideally in industry and/or in a customer-facing context.
- Knowledge of corn agronomy and corn disease modeling, e.g hybrid disease susceptibility, residue and rotation effects, canopy microclimate, and fungicide modes of action and application windows, is preferred.
- Software Knowledge: Python, the Python scientific stack, and experience with common development tools and services, such as version control software, package managers, CI/CD pipelines and cloud infrastructure.
Nice To Haves
- Relentlessly strive to improve our models, with a focus on improving the accuracy and reliability of models for corn disease and pests.
- Expertly navigate the relevant technical literature with a view to support modeling capabilities and product needs.
- Effectively communicate (verbally and in writing) to present your work and relay underlying technical concepts to non-engineers, including agronomists and growers.
Responsibilities
- Build and maintain statistical and mechanistic models for corn disease and insect pressure, including tar spot, gray leaf spot, southern rust, and key lepidopteran pests.
- Create and assess the performance of statistical and machine learning models for corn disease risk and spray timing, validating against field observations from our Midwest research network and our plants.
- Connect model output with InnerPlant's fluorescence detections, weather, and corn phenology to produce field- and zone-level recommendations.
- Collaborate with other team members to build pipelines for data input and model output.
Benefits
- insurance benefits
- flexible time off
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
