Field Data Scientist

About Inari…

Inari is the SEEDesign™ company. We embrace the diversity and complexity of nature in every aspect of our business to drive innovation – to push the boundaries of what is possible. Through our unrivaled technology platform, Inari uses predictive design and advanced multiplex gene editing to develop step-change products. We are taking a nature positive approach to unlock the full potential of seed that will transform the food system. The results will lead to more productive acres delivering value creation for farmers and a more sustainable future for our planet.

Our success is dependent on great minds, collaborating to generate bright ideas and deliver exceptional outcomes.

We have over 300 employees, with research sites in Cambridge, MA (USA) and Ghent (Belgium), as well as a product development site in West Lafayette, IN (USA). We’ve deliberately built a team that brings diversity of thought to all aspects of our business, to generate new ideas, approaches, and ways of operating. And we’ve intentionally combined experience with potential, bringing agriculture industry experts with the desire to innovate together with bright minds from academia, human therapeutics, software, and consulting. If you want to be part of a diverse and inclusive team developing unique solutions to feed the world while protecting our planet’s natural resources, we’d love to hear from you!

We are seeking a dedicated and enthusiastic field data scientist to join our team in West Lafayette, Indiana.  The field data scientist’s primary responsibilities relate to writing and maintaining code in various computer languages to support agricultural field trials. A background in coding is required, but this position is open to those without industry experience if adequate knowledge can be demonstrated through academic research or projects.  

As a field data scientist, you will… 

• Maintain and refine codebases related to data pipelines and ensure data pipelines are running correctly. 
• Write new data pipelines or extend existing data pipelines as needed. 
• Write code to process, analyze, and visualize various types of data as needed. 
• Adhere to coding best practices, including version control and containerization. 
• Write documentation and instructions for codebases and data pipelines to ensure transparency and knowledge transfer. 
• Communicate with and explain your work to technical and non-technical team members. 
• Assist others with using and understanding datasets and data pipelines as needed. 
• Assist with general field trials operations such as trial creation, data management, etc., as needed. 
• Be an active participant in teams and projects. 

You bring…  

• B.S. or M.S. degree in an engineering, mathematics, computer science, or agriculture field. 
• Minimum of at least two years of applied coding experience, such as through projects, academic research, or industry experience, preferably in Python, R, or JavaScript. 
• A desire to be part of a culture of best practices and continuous improvement.  
• The ability to handle multiple tasks and meet strict deadlines in an often-changing production environment. 
• Possess strong communication skills, both written and oral to effectively communicate with key stakeholders. 

• Show initiative, have good judgment and be willing to take on additional tasks and responsibilities as needed to achieve goals and deliverables.  
• Positive attitude and good interpersonal skills  
• A drive to challenge yourself and learn new things.  

Bonus Qualifications:  

• Experience with version control and containerization of code. 
• Experience with cloud services. 
• Knowledge of agricultural field trials. 
• Experience with GIS systems. 

FOR U.S. CANDIDATES: Please note that we use the resume you submit with your application during our background check process. To ensure an efficient and accurate background verification, we kindly ask that you carefully review and accurately represent your work history, education and other relevant information on your resume. Any discrepancies or inaccuracies found during the background check may impact your candidacy for the position.