Role Description This is a part-time, remote Machine Learning Engineer role focused on building and improving data-driven models that support trading and operations for agricultural products. The Machine Learning Engineer will design, implement, and optimize algorithms for price forecasting, demand prediction, and pattern detection in market and supply chain data. Daily tasks include cleaning and preparing datasets, experimenting with different model architectures, training and evaluating models, and deploying them into production or decision-support tools. The role also involves collaborating with trading, operations, and leadership to translate business questions into analytical solutions, documenting models and results, and monitoring model performance over time to refine and maintain accuracy.
Qualifications
Strong foundation in Computer Science and Algorithms, with the ability to design efficient, scalable solutions.
Proficiency in Statistics and Pattern Recognition to analyze complex datasets and extract meaningful insights.
Hands-on experience with Neural Networks and modern machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Practical programming skills in Python or a similar language, including experience with data processing libraries (e.g., pandas, NumPy).
Experience working with time-series, pricing, or demand forecasting data; exposure to commodities or agriculture is a plus.
Ability to work independently in a remote setting, manage part-time hours effectively, and communicate clearly with non-technical stakeholders.
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience.

Machine Learning Enginee

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