What if nature could be harnessed to help farmers sustainably feed the planet? Since 2014, Indigo has questioned agriculture's full value chain to improve grower profitability, environmental sustainability, and consumer health. The company’s scientific discoveries and digital innovations have amplified new value from soil to sale, benefiting more than 10,000 growers to date. Indigo is also the company behind The Terraton Initiative, a global effort to drawdown one trillion tons of atmospheric carbon dioxide by unlocking the potential of agricultural soils. In 2019, Indigo was ranked #1 on CNBC’s Disruptor 50 list. Headquartered in Boston, MA, Indigo has additional offices in Memphis, TN; Research Triangle Park, NC; Sydney, Australia; Buenos Aires, Argentina; Basel, Switzerland; and São Paulo, Brazil. 

Responsibilities:

• Lead teams of data scientists to solve complex agricultural problems by unifying high throughput laboratory data with mass field data with machine learning models to deliver on business scientific 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 Indigo’s microbial and agronomic treatments in real world conditions to further our understanding of where, why, and how our products work
• Build machine learning models that predict business and scientifically relevant field outcomes and simultaneously increase our understanding of real-world agricultural data and the drivers of the outcomes we observe
• Help to drive research and development 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
• Comfortable working with messy datasets; able to draw appropriate conclusions from ambiguous data and recommend future course of actions
• Strategic, able to quickly prioritize and to lead a team with self-direction; happy to both teach and mentor teammates and learn new techniques
• Thrives in a fast-paced, growth environment
• Collaborates effectively across diverse teams; passionate about teaching quantitative skills to laboratory and field scientists and continually increase the level of sophistication in experimental design and analysis
• Strong desire to continue learning, identifying new techniques and technologies, and rapidly implementing them to keep Indigo at the cutting edge

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
• 2+ years experience leading teams and complex projects
• 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