Agronomic Modeler (Corn)

InnerPlant•Davis, CA

10d

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, providing farmers and agronomists with critical and timely insights and removing guesswork from key in-season decisions.

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.
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.
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

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.

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.