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Data Analyst - Python and Statistical Modeling at Bunge · Chesterfi…
Skills
Job Description
| City : Chesterfield | State : Missouri (US-MO) | Country : United States (US) | Requisition Number : 47239 |
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Bunge is looking for a Data Scientist with in-depth Python coding and statistical modeling experience. A background in Agriculture & Economics is a plus.
The formal title for this position is "Data Scientist".
A Day In The Life: Leveraging our inherent market intelligence is a critical component to Bunge’s success, particularly in the dynamic agricultural markets. This the reason why Bunge has one of the large economic analysis teams in the industry. Our analysis team is comprised of over 50 analysts world‐wide who gather, analyze, supply and demand and other pertinent information. The global analysts work closely with global traders to help market develop market theses that drive the company’s trading and risk decisions. The team covers global grains, oilseeds, biofuels, ocean freight and livestock.
The Data Scientist will be an integral part of the Bunge Economic Analysis team, leveraging advanced statistical modeling, econometrics, and machine learning to analyze vast internal and external datasets. This role is crucial for developing sophisticated predictive models that inform our understanding and forecasting of global commodity market dynamics, including crop production, pricing trends, and customer behavior, thereby advancing our economic research functions worldwide.
What You'll Be Doing: • Collaborate effectively within cross-functional teams, including economists, market analysts, data engineers, and business leaders, to translate complex business challenges into solvable data science problems. • Translate complex business problems into data-driven analytics and machine learning tasks, then design, develop, and swiftly deploy high-performance, resilient predictive models using a range of machine learning, statistical, and econometric techniques. • Design and implement advanced analytical strategies and algorithms to extract, analyze, and leverage diverse data sources. Critically assess the effectiveness, accuracy, and suitability of various data inputs for global economic models. • Rigorously monitor, evaluate, and refine the performance of deployed machine learning solutions to ensure sustained accuracy and measurable business impact. • Clearly and effectively communicate complex analytical findings, model insights, and strategic recommendations to diverse audiences, including senior leadership, traders, and business units, supporting informed decision-making and global risk management.
Skill/Experience Requirements: • Minimum MS degree in Economics, Agricultural Economics, Statistics, Computer Science, Quantitative Finance, Business Analytics, or a closely related quantitative field. • Minimum 2-year of professional experience in a Data Scientist or similar quantitative role, preferably within an economic analysis, commodity trading, financial services, or agribusiness environment. • Expert proficiency in Python (e.g., pandas, NumPy, scikit-learn, statsmodels, TensorFlow) for data manipulation, statistical analysis, machine learning, and data visualization. • Strong SQL skills for data extraction, manipulation, and analysis from relational and non-relational databases. • Solid understanding of statistical inference, econometric modeling (e.g., time series analysis, causal inference), and machine learning algorithms (e.g., regression, classification, clustering, tree-based models). • Demonstrated ability to frame complex problems, design analytical solutions, and extract meaningful insights from large datasets. • Excellent communication and presentation skills with the ability to explain complex concepts or methods in a precise and clear manner. • Detail-oriented, proactive, self-motivated, build work relationships, and able to work both independently and collaboratively in a fast-paced, dynamic global environment.
Preferred Experience/Skills: • 5+ years of industry work 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 (e.g., Google Cloud Platform, AWS). • Familiarity with MLOps practices for deploying, monitoring, and maintaining machine learning models in production. • Specific knowledge of agricultural commodity markets (e.g., grains, oilseeds, biofuels), agronomics, etc.
Bunge offers a variety of benefits including health and wellness plans, retirement contribution and paid vacation/holidays.
At Bunge (NYSE: BG), our purpose is to connect farmers to consumers to deliver essential food, feed and fuel to the world. As a premier agribusiness solutions provider, our team of ~34,000 dedicated employees partner with farmers across the globe to move agricultural commodities from where they’re grown to where they’re needed—in faster, smarter, and more efficient ways. We are a world leader in grain origination, storage, distribution, oilseed processing and refining, offering a broad portfolio of plant-based oils, fats, and proteins. We work alongside our customers at both ends of the value chain to deliver quality products and develop tailored, innovative solutions that address evolving consumer needs. With 200+ years of experience and presence in over 50 countries, we are committed to strengthening global food security, advancing sustainability, and helping communities prosper where we operate. Bunge has its registered office in Geneva, Switzerland and its corporate headquarters in St. Louis, Missouri. Learn more at Bunge.com.
Every day our people exemplify these values, which represent Bunge at its core:
• We Are One Team – Collaborative, Respectful, Inclusive • We Lead The Way – Agile, Empowered, Innovative • We Do What’s Right – Safety, Sustainability, With Integrity
If this sounds like you, join us! We value and invest in people who believe in our purpose and are excited to live it every day – people who are #ProudtoBeBunge