Indigo is a company dedicated to harnessing nature to help farmers sustainably feed the planet. With a vision of creating a world where farming is an economically desirable and accessible profession, Indigo works alongside its growers to apply natural approaches, conserve resources for future generations, and grow healthy food for all. Utilizing beneficial plant microbes to improve crop health and productivity, Indigo’s portfolio is focused on cotton, wheat, barley, corn, soybeans, and rice. The company, founded by Flagship Pioneering, is headquartered in Boston, MA, with additional offices in Memphis, TN, Research Triangle Park, NC, Sydney, Australia, Buenos Aires, Argentina, and São Paulo, Brazil. www.indigoag.com

Responsibilities:

• Lead teams of data scientists to solve complex agricultural problems with mass field data and deliver on business priorities.
• Perform end-to-end analysis from development of data specification requirements through data processing and normalization and statistical/probabilistic modeling to machine learning on an ongoing basis
• Determine causal effects of agricultural interventions
• Build machine learning models that predict business relevant field outcomes and simultaneously increase our understanding of mass agricultural data.
• Contribute to business and sales recommendations by designing and developing visuals of data analyses and presenting these as compelling storyboards to non-technical audiences.

Competencies:

• Skilled at working complex analysis problems, identifying and appropriately applying advanced statistical analysis techniques and machine learning methods as necessary and inferring causal treatment effects.
• A strategic mind, able to quickly prioritize and to lead a team with self-direction. Happy to both teach and mentor teammates and learn new techniques.
• Comfortable working with messy agricultural and biological datasets from a variety of non-normalized sources and imbued with high level of ambiguity.
• Interact effectively with a diverse set of teams including agronomy, engineering, sales, logistics, operations research, R+D, and project management.
• Able to draw appropriate conclusions from ambiguous data and recommend future course of actions.
• Comfort and experience leading data science projects and teams

Qualifications:

• PhD degree in a quantitative discipline (e.g. statistics/applied math, physics, engineering, quantitative biology) or equivalent practical experience.
• 3+ years of industry experience in statistical and machine learning analysis of complex datasets.
• Experience leading data science and/or cross functional teams. 
• Advanced programming experience in Python (preferred) or R. Working experience with a wide range of statistical, geospatial and machine-learning libraries (e.g scikit-learn, statsmodels, etc).
• Experience working with databases (SQL), structuring data, and scripting languages.
• Experience working in cloud systems such as AWS; fluency with the command line.