Agronomic Modeler (Corn)

InnerPlant is on a mission to transform farming as never before imagined:  We give crops a voice by turning plants into living sensors that signal the onset of stress, enabling farmers to increase yields and reduce costs by optimizing inputs.

We are seeking an Agronomic Modeler to refine and continuously improve the corn disease and pest models behind our commercial product, CropVoice.  The CropVoice platform translates signals from our 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. 

As an Agronomic Modeler, you 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.  You will work collaboratively with the InnerPlant team to improve our  models and explain resulting data and recommendations clearly.  To achieve success in the role, you will:

• 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. 

The successful candidate  will be a self-starter with a drive for continuous improvement and who works collaboratively and transparently with the team.  You will have experience and deep knowledge of modeling in an ecological or biophysical context, including statistical modeling, machine learning, and geostatistics.  In particular, you will:

• 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.

Your Qualifications

• 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.