Data Analyst - Python and Statistical Modeling

Bunge•Chesterfield, MO

About The Position

The Data Scientist is an integral part of Bunge’s Economic Analysis team, which gathers and analyzes information on global grains, oilseeds, biofuels, ocean freight, and livestock. The role applies advanced statistical modeling, econometrics, and machine learning to large internal and external datasets to develop predictive models that support analysis and forecasting of global commodity market dynamics, including crop production, pricing trends, and customer behavior. The work advances Bunge’s economic research and informs trading and risk decisions worldwide. The Data Scientist works with economists, market analysts, data engineers, and business leaders to turn business challenges into data science problems. The position develops, deploys, monitors, and refines predictive models, assesses data sources for use in global economic models, and communicates findings and recommendations to senior leadership, traders, and business units.

Requirements

Master’s degree or higher in Economics, Agricultural Economics, Statistics, Computer Science, Quantitative Finance, Business Analytics, or a closely related quantitative field.
At least 2 years of professional experience in a Data Scientist or similar quantitative role; experience in economic analysis, commodity trading, financial services, or agribusiness is preferred.
Expert proficiency in Python, including tools such as pandas, NumPy, scikit-learn, statsmodels, and TensorFlow, for data manipulation, statistical analysis, machine learning, and data visualization.
Strong SQL skills for extracting, manipulating, and analyzing data from relational and non-relational databases.
Solid understanding of statistical inference, econometric modeling such as time-series analysis and causal inference, and machine learning algorithms such as regression, classification, clustering, and tree-based models.
Ability to frame complex problems, design analytical solutions, and extract meaningful insights from large datasets.
Excellent communication and presentation skills, including the ability to explain complex concepts and methods clearly and precisely.
Detail-oriented, proactive, self-motivated, able to build work relationships, and capable of working independently and collaboratively in a fast-paced, dynamic global environment.

Nice To Haves

At least 5 years of industry experience in Data Science fields.
Experience with geospatial data analysis, remote sensing, satellite imagery processing, and deep learning for statistical modeling.
Experience with big data technologies and cloud-based data platforms and products such as Google Cloud Platform or AWS.
Familiarity with MLOps practices for deploying, monitoring, and maintaining machine learning models in production.
Knowledge of agricultural commodity markets, including grains, oilseeds, and biofuels, and agronomics.
Background in agriculture.

Responsibilities

Collaborate with economists, market analysts, data engineers, and business leaders to translate business challenges into data science problems.
Design, develop, and deploy high-performance, resilient predictive models using machine learning, statistical, and econometric techniques.
Design and implement analytical strategies and algorithms to extract, analyze, and leverage diverse data sources.
Assess the effectiveness, accuracy, and suitability of data inputs for global economic models.
Monitor, evaluate, and refine deployed machine learning solutions to sustain accuracy and achieve measurable business impact.
Communicate analytical findings, model insights, and strategic recommendations to senior leadership, traders, and business units to support decision-making and global risk management.