YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Data Science Intern, are to: 

• Utilize new technologies and collaborate closely with team members and partners within the business;
• Responsibilities may include building and validating predictive models incorporating key breeding data, soil, weather and environmental data.  Demonstrate the application of the models and deploy them to stakeholders;
• Data Science roles in the past have specified into Image Science, Operations Research, Data Visualization, Genomics, Geospatial and other disciplines;
• Internship opportunities for this assignment include May 18th, 2020 through August 7th, 2020 OR June 15th, 2020 through September 1st, 2020.

WHO YOU ARE

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:

• Current enrollment at the University of Illinois Urbana-Champaign;
• Advanced degree MS/PhD student in Computer Science, Statistics, Data Science, Mathematics, or Computational Biology;
• Proficiency in Machine Learning and Statistics algorithms and concepts (Mixed Models, Random Forest, Deep Learning, SVM, etc.);
• Strong programming skills in R, Python, SQL, Java or Linux;
• Experience with cloud computing such as Hadoop and Spark;
• Problem solving ability and experience in analyzing and presenting complex data;
• Organizational, interpersonal, and written communication skills;
• Strong business aptitude, the ability to rapidly learn new problem domains, and become conversant in the domain with subject matter experts;
• Ability to communicate complex ideas in a clear, precise and actionable manner;
• Detail and results oriented with the ability to work independently;
• Able to work 40 hours per week if interning during the summer.

Preferred Qualifications:

• Willingness to extend own data science and statistical interests to agriculture and/or experience working with agricultural/biological scientific data.