Monsanto Data Scientist - Geospatial Intern/Co−op in ST. LOUIS, Missouri

Monsanto is passionate about using science and technology to improve agriculture. Technology continues to evolve at an ever-rapid rate, and it is foremost to Monsanto’s success today and in the future to evolve as well. The Global IT Analytics team within Products and Engineering looks to find creative and innovative solutions to enable success within our research pipeline while identifying and capitalizing on new opportunities to maintain and grow our position as the 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 our organization.

We currently have an opportunity available for a Geospatial Data Scientist to join our Global IT Analytics team within the Products and Engineering organization in IT.

The successful candidate will use the latest mathematical and statistical innovations to propose analytic solutions that accommodate emerging trends in data quantity and quality. The Data Scientist will design and test algorithms and conduct prototyping to evaluate possible scenarios leveraging computational and statistical techniques for the development of novel approaches for high-throughput big data analyses. The successful candidate will have the opportunity to work collaboratively with interdisciplinary scientists internal and external to Monsanto, organize challenging problems, develop new solutions, and work with business & development teams to ensure these solutions deliver value.

Required Skills:

  • Pursuing a PhD 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 & influencing people at varying levels of responsibility

Desired Skills:

  • Experience building Spatio-Temporal models and predictive models is highly desired

  • 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 is highly desired

  • 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, C+ Organization: GLB IT P&E Analytics51177654_

Title: Data Scientist - Geospatial Intern/Co−op

Location: North America-USA-Missouri-St. Louis

Requisition ID: 01KK1

Job: Information Technology

Schedule: Full-time

At Monsanto, we value a diverse combination of ideas, perspectives and cultures. All qualified applicants will receive consideration for employment without regard to, among other things, race, religion, color, national origin, age, sex, sexual orientation, gender identity, gender expression, status as a protected veteran, or status as a qualified individual with a disability. If you need a reasonable accommodation to access the information provided on this website, please for further assistance.access our disability accommodations process

U.S. Employment Law (applicable for U.S. jobs ONLY): EEO Employer Minorities/Females/Protected Veterans/Disabled. For information about equal employment opportunity rights, please refer to this information from the U.S. federal government: ; .

EEOC PosterEEOC Supplement