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, grow healthy food for all, and conserve resources for future generations. Utilizing beneficial plant microbes to improve crop health and productivity, Indigo’s portfolio is focused on cotton, wheat, rice, corn, and soybeans. The company is headquartered in Boston, MA, with commercial and customer service based in Memphis, TN. www.indigoag.com 

The Data Scientist, Agronomy will enable optimal data-driven decision making by both Indigo and its customers, dramatically improving farmers’ outcomes. This role will analyze and model expected crop yield under various scenarios, and produce optimal recommendations based on those models. Using modern cloud-scale computational tools and machine learning algorithms, the Data Scientist, Agronomy will construct preprocessing pipelines, predictive models, optimization algorithms, and decision support systems. 

 

Outcomes:

  • Using state-of-the-art machine learning, statistical, and cloud computing, model crop yields based on:
    • Crop & variety
    • Microbial treatments
    • Climate & environmental conditions
    • Satellite and UAV multispectral images
    • Agronomic inputs
    • Farm practices
    • Field and regional history
    • Agricultural stresses and pressures
  • Develop prescriptive tools that assist growers in making optimal on-farm planting and farm management decisions including:
    • Economically optimal cropping strategies, planting prescriptions, field treatments, and harvesting
    • Cost-benefit and financial analyses
    • Compelling and clear data visualizations
  • Partner closely with Indigo Software Technology to turn Data Science products into best-in-class end-user applications.

 

Competencies/Qualifications:

  • 6-10 years agriculture industry experience required.
  • Masters or PhD Degree in a relevant field such as data science, computer science, mathematics, statistics, quantitative agronomy, environmental science, applied physics or engineering.
  • Heavy experience in machine-learning and statistical crop yield modeling, forecasting, decision support, and economic optimization.
  • Outstanding communication skills - Outgoing and enthusiastically enjoys explaining statistical analyses and methods to customers and decision makers throughout the organization.
  • Experience in and/or eagerness to continue to learn about agriculture, agronomy, GIS, spatial analyses, plant biology, microbiology, agricultural economics and financial hedging, and the plant microbiome.
  • High fluency with Python, R, Spark, GIS, and Bayesian MCMC tools such as JAGS or STAN. Strong ability to produce lucid graphical plots, maps, and highly interactive visualizations. Experience in Linux, AWS, ec2, S3, Spark, and/or H2O.
  • Team player – someone who thrives working in cross functional teams throughout the organization.
  • A curious, scientific mind dedicated to teasing apart new results through careful, quantitative analysis joined with sound scientific practice.
  • Extremely strong customer focus, seeing grower success as our fundamental goal.
  • Experience working in agriculture industry and a dedication to Data Science performed in an industrial setting.
  • Solid understanding of modern Bayesian statistics, ensemble classifiers, deep learning, and other machine learning techniques. Experience with model validation and evaluation, and data imputation.
  • Familiarity with NoSQL columnar databases as well as traditional SQL databases. Experience with MILP, combinatorial optimization, metaheuristics, and other types of optimization are an added advantage.

 

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