Bayer Crop Science is passionate about using Data Science and Information Technology to improve agriculture. Data Science is critical to the success of Bayer Crop Science today and in the future as we transform into an Information Services company. We are seeking a Data Scientist to join our IT Decision Science team as an intern/co-op in the area of genome analytics. The IT Decision Science team looks to find creative and innovative solutions to enable success within our R&D, supply chain and commercial organizations while identifying and capitalizing on new opportunities to maintain and grow our position as a digital agriculture market leader. The ability to examine and optimize our current methods coupled with an eye on the future lends itself to a unique opportunity within Bayer Crop Science and our team. This is an exciting opportunity for a talented and passionate individual to join a multidisciplinary team of data scientists and engineers who work on real world problems.
The primary responsibilities of this role, Data Scientist Co-Op, are to: 

  • Use the latest mathematical and statistical innovations to propose analytic solutions that accommodate emerging trends in data quantity and quality;
  • Design and test algorithms and conduct prototyping to evaluate possible scenarios leveraging computational and statistical techniques for the development of novel approaches for high-through-put big data analyses;
  • Work collaboratively with interdisciplinary internal and external scientists, organize challenging problems, develop new solutions, and work with business & development teams to ensure these solutions deliver value.

Your success will be driven by your demonstration of our LIFE values.  More specifically related to this position, Bayer seeks an incumbent who possesses the following:
Required Qualifications:

  • Pursuing a PH.D. in Mathematics, Statistics, Spatial Statistics, Geoinformatics, Environmental Modeling, Engineering, Computer Science, or Computational Biology;
  • Proficiency in Spatial and/or Temporal Statistical Modeling;
  • Experience with statistical modeling and data mining;
  • Experience analyzing and presenting complex data, and proven problem solving abilities;
  • Experience with Statistical and Mathematical programming packages (R, Matlab, Python);
  • Strong publication record in leading scientific journals;
  • Strong organizational, interpersonal, and written communication skills;
  • Ability to work in a matrix environment, leading and influencing people at varying levels of responsibility.
      Preferred Qualifications:


  • Experience building Spatio-Temporal models and predictive models;
  • Expertise in Spatial auto-correlation modeling, Bayesian Statistics, Spatial Econometrics, or Topological data analysis;
  • Pattern recognition (conditional random fields, hidden Markov models, maximum entropy Markov Models, etc.);
  • Proficiency in machine learning, algorithms and concepts (ensembles, deep learning, SVM, etc.);
  • Experience in stochastic modeling and simulation;
  • Experience working with agricultural/biological scientific data;
  • Drive for translating business problems into research initiatives that deliver business value;
  • Creativity in defining challenging exploratory projects;
  • Ability to code in Java, C, Hadoop, MapReduce, PySpark, Spark.


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