1-844-696-6465 (US)        +91 77600 44484        help@dezyre.com

Data Scientist

Company Name: Syngenta
Location: Slater, IA
Date Posted: 22nd Nov, 2016
Description:

In this role, you are required to use quantitative genetics, GIS (geographic information systems), advanced mathematical models, operations research techniques, simulation and strong business acumen to deliver insight, recommendations and solutions for Seeds Product Development science and business problems. You are required to formulate and apply mathematical modelling and other optimizing methods to develop and interpret information that assists management with decision making, policy formulation or other managerial functions.  

Accountabilities

  • Serve as consulting resource for quantitative genetics, GIS, experimental design and data analysis methodologies for target Seeds Product Development scientists
  • Prepare and conduct training to Seeds R&D community on relevant topics of statistics and data science
  • Maintain expertise in the proper application and use of computational tools for the management of germplasm materials, data management and analysis, and reporting, for activities of Seeds Product Development
  • Serve as “power user” subject matter expert for the ongoing development and optimization of at least one critical computational application used by Seeds Product Development
  • Serve as the user support for users within the defined SPD work group, for proper application and use of computational tools for the management of germplasm materials, data management and analysis, and reporting, for activities of Seeds Product Development
  • Conduct regular new user and refresher trainings on selected computational tools to the SPD staff
  • Communicate opportunities for improvement of computational applications to the development or maintenance teams, based on personal use or user feedback
  •  
Qualification:

Essential Skills & Knowledge

  • Theoretical and practical knowledge of quantitative genetics, GIS, operations research and mathematical optimization concepts is a must
  • Strong business aptitude, the ability to rapidly learn new problem domains, and become conversant in the domain with subject matter experts
  • Strong organizational, interpersonal, and proven problem solving abilities
  • Ability to work in a matrix environment, leading & influencing people at varying levels of responsibility
  • Proven ability to communicate complex qualitative analysis in clear, precise and actionable manner
  • Creative, proactive, bold and out-of-box thinking
  • Good communication skills
  • A good understanding of algorithms and computational complexity is desirable
  • Creativity in defining challenging exploratory projects and developing solutions

Qualifications

  • BS or MS completion in 6 months or Masters with equivalent experience in Quantitative Genetics, GIS, Operations Research, Industrial Engineering, or Mathematics or a related field
  • Advanced degree in statistics, data science, or computational biology
  • Analytical thinking skills
  • Strong influencing skills to lead alignment and adoption on process and methods
  • People skills that will build bridges
  • Experience with machine learning algorithms and statistical techniques is highly desired
  • Expertise in probability theory, queuing theory, game theory is desirable
  • Drive for translating business problems into research initiatives that deliver business value
  • Expertise in many of the following: Monte Carlo simulations, decisions analysis, stochastic models, system dynamics and forecasting
  • Experience and passion for solving analytical problems involving big data sets using quantitative approaches to generate insights from data
  • Successful experiencing in working in teams
  • Expertise in at least one or more of the following: network optimization, stochastic programming, portfolio optimization, efficient optimizer design for high dimensional convex/non-convex problems, optimal scheduling and routing, multi objective optimization

Additional Information

  • Occasional travel within region
  • Potential infrequent travel outside of region