ASA Connect

Analytics Automation Intern/Coop

If you love developing innovative, breakthrough technologies that create breakthrough products, you’ll want to check out this exciting opportunity in data science and analytics at Monsanto (www.monsanto.com)

We are seeking exceptionally talented graduate-level students who share our passion for innovation to be part of our Biotech Trait Analytics & Decision Science Team at our world headquarters in St. Louis, Missouri. This team has access to data you’ve only dreamt about and is driving creation of predictive, prescriptive models that will shape the industry. We work on hard problems because we love the challenge.  As part of our diverse, highly dynamic group, you will work side-by-side with a team of exceptional data scientists with diverse backgrounds (Statisticians, Mathematicians and Engineers) to foster your career growth and development while delivering next-generation scientific breakthroughs. Our openings have the flexibility to be either a 3 month (intern: May – August 2016) or 6 month (co-op: May – Nov 2016) assignment.

Shift from theory-based statistical programming to analytics modules that excel in computational efficiency in terms of speed, accuracy, and robustness. This is the transition we expect from the Analytics Automation Intern/Coop, who will apply cutting age computational techniques and algorithms to develop R modules for modeling and analysis of data from our agricultural experiments.  You will work closely with members of the Trait Testing Analytics Team and scientists from Information Technology. 

• Candidates must be currently enrolled in a Masters or PhD degree program in Computational Statistics, Computational Mathematics, Computer Science, or Computational Biology. Graduate students in Statistics, Mathematics, Biostatistics, or Bioinformatics with strong background in development of efficient computer algorithms are also eligible
• Proficiency in R programming with experience in development of R packages with computational efficiency
• Experience in interfacing R with high level programming languages such as Python and MATLAB to enhance computational efficiency
• Expertise in numerical methods for dealing with large and high dimensional unbalanced data structure
• Basic knowledge of Analysis of Variance, Mixed Model, Regression and other statistics tools and the implementation of these techniques in R
• Strong communication skills for effective interactions with senior business/R&D stakeholders as well as peer groups and team members
• Experience with numerical linear algebra for solving linear systems with sparse and ill-conditioned matrices is highly desirable
• Some background and training in crop science area is desirable