Data Scientist at Bunge in Chesterfield, MO
Overview:
The data scientist will be an integral part of the Bunge Economic Analysis team analyzing large Bunge internal and publicly available data sets and developing advanced modelling techniques using best practices in Machine Learning for advancing the Global economic research functions in forecasting market dynamics inclusive of crop production, pricing and customer behavioral analysis globally. Core Functions:
Work in a cross-disciplinary project team of database specialists, data scientists, and business subject-matter experts. Gain in-depth understanding of business problems from subject-matter expects and identify and analyze the relevant variables that affect global commodity markets and their components; Translate business problems into data-driven analytics / machine-learning tasks and swiftly develop and deploy high-performance and resilient machine learning based solutions. Develop predictive models using machine learning, statistical and econometric tools. Monitor the performance of machine learning based solutions to ensure business impact. Design strategies and implement algorithms to analyze and leverage data, assessing the effectiveness and accuracy of data sources to be used as inputs to developing global models. Present findings to a large group of business users; effectively summarize and communicate results to the global group for risk management. Qualifications/Requirements:
Minimum MS degree in a quantitative field (physics, statistics, engineering, computer science, math, econometrics, etc.). Three or more years of research or industry experience in machine learning, pattern recognition, time series analysis, deep learning, and related data-driven fields. Expertise in analytical packages such as Python, R, C/C++, PyTorch, or Tenserflow. Intellectually curious and creative, willing to share knowledge, and adaptive to new techniques. Strong communication skills, critical thinking, attention to detail, and business acumen. Solid foundation in mathematics and statistical modeling to be able to solve complex business problems via statistical models, machine learning algorithms, text analytics/NLP etc. Preferred Experience/Skills:
Direct industry or educational experience in agricultural economics. Understanding of agronomy, weather, remote sensing, GIS, economics, and finance Publication on top journals and conferences Experience with large-scale database and distributed computing Participation in open source community
Salary Range:
$80K -- $100K
Minimum Qualification
Data Science & Machine LearningEstimated Salary: $20 to $28 per hour based on qualifications.
The data scientist will be an integral part of the Bunge Economic Analysis team analyzing large Bunge internal and publicly available data sets and developing advanced modelling techniques using best practices in Machine Learning for advancing the Global economic research functions in forecasting market dynamics inclusive of crop production, pricing and customer behavioral analysis globally. Core Functions:
Work in a cross-disciplinary project team of database specialists, data scientists, and business subject-matter experts. Gain in-depth understanding of business problems from subject-matter expects and identify and analyze the relevant variables that affect global commodity markets and their components; Translate business problems into data-driven analytics / machine-learning tasks and swiftly develop and deploy high-performance and resilient machine learning based solutions. Develop predictive models using machine learning, statistical and econometric tools. Monitor the performance of machine learning based solutions to ensure business impact. Design strategies and implement algorithms to analyze and leverage data, assessing the effectiveness and accuracy of data sources to be used as inputs to developing global models. Present findings to a large group of business users; effectively summarize and communicate results to the global group for risk management. Qualifications/Requirements:
Minimum MS degree in a quantitative field (physics, statistics, engineering, computer science, math, econometrics, etc.). Three or more years of research or industry experience in machine learning, pattern recognition, time series analysis, deep learning, and related data-driven fields. Expertise in analytical packages such as Python, R, C/C++, PyTorch, or Tenserflow. Intellectually curious and creative, willing to share knowledge, and adaptive to new techniques. Strong communication skills, critical thinking, attention to detail, and business acumen. Solid foundation in mathematics and statistical modeling to be able to solve complex business problems via statistical models, machine learning algorithms, text analytics/NLP etc. Preferred Experience/Skills:
Direct industry or educational experience in agricultural economics. Understanding of agronomy, weather, remote sensing, GIS, economics, and finance Publication on top journals and conferences Experience with large-scale database and distributed computing Participation in open source community
Salary Range:
$80K -- $100K
Minimum Qualification
Data Science & Machine LearningEstimated Salary: $20 to $28 per hour based on qualifications.
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