Bunge is looking to leverage advanced statistical modeling, econometrics, and machine learning to analyze vast internal and external datasets and develop sophisticated predictive models that inform their understanding and forecasting of global commodity market dynamics.
Requirements
- 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).
- 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.
Responsibilities
- 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.
- 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.
Other
- 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.
- 5+ years of industry work experience in Data Science fields.
- Detail-oriented, proactive, self-motivated, build work relationships, and able to work both independently and collaboratively in a fast-paced, dynamic global environment.
- Excellent communication and presentation skills with the ability to explain complex concepts or methods in a precise and clear manner.
