Research Agronomist – Data & Modeling

Pivot Bio•Creve Coeur, MO

5d•Onsite

About The Position

Fueled by an innovative drive and a deep understanding of microbiology, genomics, crop nutrition and agriculture, Pivot Bio is pioneering game-changing advances in fertilizer technology. Our first commercial product harnesses the power of naturally-occurring microbes, modern gene editing and application technologies to provide nitrogen to crops. We are dedicated to providing new solutions for farmers to improve yield as they work to help feed the world’s growing population. Read/Hear more about Pivot Bio on Forbes or PBS News Hour. The Research Agronomist – Data & Modeling will report to the Associate Director of Data Operations and Modeling. This is a research-first role with strong statistical grounding and advanced computational skills operating in ambiguity and independently driving high value work. This role operates at the intersection of agronomic research, spatial analytics, and product innovation, transforming large geospatial datasets into actionable insights that shape research direction, product strategy, and operational decisions. This position will strengthen analytical capability to directly drive Pivot Bio’s product innovation pipeline and research strategy. This role will analyze data, shape decisions, design new analytical approaches, and help elevate how we use data across the organization. This role will be onsite at our Creve Coeur, MO or Minnetonka, MN location.

Requirements

Advanced Python (pandas, numpy, geopandas, rasterio, scikit-learn).
Strong SQL skills (Databricks or similar cloud warehouse experience preferred).
Experience processing large geospatial raster datasets.
Strong understanding of applied statistics and experimental design.
Experience with feature engineering, modeling, and uncertainty quantification.
Familiarity with cloud compute environments (AWS preferred).
Experience building reproducible analytical workflows.
Experience using GitHub
Researcher mindset: curious, rigorous, and skeptical.
Strong intuition for data quality and anomaly detection.
Comfortable operating in ambiguity and prioritizing highest-impact work.
Tenacious and self-directed, yet collaborative.
Clear communicator who can translate complexity into action
Master’s degree (or equivalent experience) in Agronomy, Statistics, Data Science, Geospatial Analytics, or related field
2–4 years of experience in applied research, spatial analytics, or agronomic data science
Experience working with satellite-derived datasets.
Must be authorized to work in the United States

Nice To Haves

Background in agricultural field trial analysis preferred.

Responsibilities

Identify high-impact opportunities and independently initiate exploratory analyses.
Prototype analytical methods to support Research Agronomy and Product Innovation
Quantify uncertainty and guide risk-aware decisions.
Engineer features from remote sensing data for modeling applications.
Perform spatial analysis using Python, QGIS, and/or ESRI tools.
Analyze plant growth dynamics across seasonal time periods.
Perform EDA, field trial analysis, sensitivity analyses, and QC metric design.
Develop and evaluate machine learning models when appropriate.
Translate statistical outputs into clear, actionable recommendations.
Use python and SQL to work within platforms such as Databricks and AWS
Build reproducible research pipelines (data → code → outputs → documentation)
Contribute to ETL processes and partner closely with engineering.
Develop high-impact visualizations, presenting concise insights with clear next steps.
Partner cross-functionally with research agronomists, product teams, and engineers.
Coach peers and contribute to a strong Research Agronomy culture.