Geospatial Data Science Intern

About Pivot Bio:  

Fueled by an innovative drive and a deep understanding of the soil microbiome, Pivot Bio is pioneering game-changing advances in agriculture. Our first commercial product harnesses the power of naturally-occurring microbes to provide nutrients to crops.  We are dedicated to providing new sustainable ways for farmers to improve yield as they work to help feed the world’s growing population. Read/Hear more about Pivot Bio on OneZero or CNN. 

Position: Geospatial Data Science Intern, Berkeley CA Headquarters or Remote, May 2021 

A crucial step in Pivot’s goal to reduce agricultural dependence on synthetic nitrogen fertilizer is to characterize the performance and sustainability impacts of our products in the field. As part of the Field Technology team you will help bring together advanced analytical tools with sensor and other geospatial data to answer questions relevant to product development, experimental design, and outreach to growers. The internship will involve development of tools to aid analysis of geospatial data, possibly including algorithms for cleaning data and characterizing spatial dependency as well as modeling environmental relationships with product performance, and visualizing results.   

Responsibilities: 

• Develop and productionize geospatial models and visualization tools to improve characterization of product field performance using a combination of remote sensing and field sample data.  
• Build and curate a repository of geospatial data layers to support field characterization at multiple scales. 
• Support statistical consultation with agronomy and laboratory teams for designing and analyzing experimental data. 

Qualifications and Experience 

• Masters or pursuing PhD in a quantitative discipline, such as engineering, ecology, or computer science. Degrees with an emphasis in agricultural or other environmental sciences preferred.  
• Experience building statistical models for geospatial data or other types of large highly-autocorrelated datasets.  
• Demonstrated ability to build organized, reproducible analytical workflows in an open-source programming language such as Python (preferred) or R.     
• Experience with open-source geoprocessing and visualization tools such as QGIS or python geospatial libraries 

• Experience with Bayesian probabilistic programming tools (Stan, pymc3) or process-based crop/soil models a plus 
• Strong communication and organizational skills 

Must be authorized to work in the United States 

What we offer: 

• Exciting opportunity contribute to and work with a talented and fun team 
• Opportunity to gain real-world, practical experience in your field of interest