ML Engineer, Commodities Applied ML

What you'll do

• Develop practical geospatial analytics and data products using satellite, aerial, and meteorological datasets.
• Translate investment team needs into testable analytical questions and data-driven solutions.
• Build, validate, and improve statistical and machine-learning models and signals.
• Develop reliable data pipelines and integrate diverse datasets for investment use cases.
• Deploy and maintain data products using cloud-native architecture.
• Write clear, reproducible Python and SQL code for peer review and scalability.
• Produce actionable insights through visualizations, dashboards, and concise written reports for stakeholders.
• Practice robust modeling hygiene by preventing data leakage, look-ahead bias, and overfitting.

What we're looking for

• Must be currently enrolled in a Master's degree program.
• Strong foundation in statistics and machine learning.
• Proficiency in Python and SQL.
• Familiarity with Git and software development practices.
• Ability to build, validate, and improve statistical and machine-learning models.
• Experience developing reliable data pipelines and datasets.
• Ability to deploy and maintain data products with cloud-native architecture in mind.
• Ability to communicate complex findings to both technical and non-technical audiences.