Advanced Agrilytics is an agronomic services company enabled by best in class digital capabilities providing growers independent, sophisticated and robust input prescriptions and operational advice.   The company has focused on the major inputs and in-crop decisions as determined by value and return on investment within the overall system. This company is well differentiated from the crowded SaaS providers in the digital ag space given its impressive multi-season results and independent, high-touch business model.  

Job Summary:

Extract insights from the agronomic, soil and environmental data we collect on our customer’s enterprises. As part of the Advanced Agrilytics data science team, your main responsibility will be to develop quantitative solutions and help support new techniques to address challenges in digital agriculture using large datasets.


  • The Data Scientist will translate clients’ data requirements into technical development encompassing data profiling, metadata enrichment, provenance and lineage, exploration, statistical analysis, data mining, machine learning, visualization, modeling, and reporting
  • Provide reports, analyses, processes and visualizations through the various company life cycles
  • Provide consulting and assistance to agronomists in the effective understanding and use of analytical outcomes and tools
  • Proposing new ideas and novel solutions that do not follow conventional thinking or approaches.
  • Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication
  • Establish procedures for the application of basic machine learning algorithms to agronomic and environmental data
  • In collaboration with the Engineering team, develop processes to implement quantitative solutions
  • Develop and support quantitative solutions and related documentation
  • Extending data resources with third party sources of information when needed
  • Lead and self-starter who can own complex projects from start to finish
  • Perform other related duties as assigned

Skills and Competencies

  • A complete Master’s degree in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Computer Engineering, or Computer Science
  • A degree in a math-related field (e.g., Computer Information Systems, Engineering, Statistics)
  • 3+ years of professional work experience
  • 3+ years of relevant industry experience with statistical tools such as SPSS, SAS, Stata, and/or other relevant predictive and modeling software
  • 3+ of relevant industry experience with common data science toolkits such as R, Anaconda Python, Julia, and Apache MADlib
  • 3+ years of relevant industry experience with data visualization tools and graphical libraries such as Tableau, Business Object, Plotly, D3.js, GGplot, etc.
  • Strong background in statistics methodology and the ability to infer causal relationships. Have taken courses such as probability, random variables, design of experiments, statistical inference, and multivariate analysis
  • Good applied statistics skills, such as a complete understanding of probabilistic distributions, ability to perform parametric and non-parametric statistical testing, regression analysis, and latent variable models
  • Strong background in computer and programing skills. Have taken courses such as algorithms, programming, data structures, data mining, artificial intelligence, machine learning, and pattern recognition
  • Good understanding (assumptions and drawbacks) of statical models and machine learning algorithms, such as generalized linear models, k-NN, Naive Bayes, tree-based methods, mixture models, SVM, random forests, neural networks, etc.


  • Data mining using state-of-the-art methods on large spatio-temporal datasets derived from agricultural production systems
  • Experience in working with large-scale spatial and temporal data
  • Experience with ArcGIS or other geographic information systems (GIS) platform would be beneficial
  • Provide successful cases of data analysis such as peer review papers, github project, or any other related result published on analytical and ML content platforms