Trading Analytics & AI Intern - Fertilizer

Summary

We’re looking for a curious, hands on intern to help us structure and ingest trading data into AI/ML workflows and refine our fertilizer trading models for illiquid markets. You’ll partner with traders and quants to turn disparate supply/demand signals into clean, reliable features, run backtests, and help operationalize models that improve price discovery and risk aware decision making in products such as MAP, DAP, SSP, and urea. 

Responsibilities

• Data ingestion & quality
  
  • Build repeatable pipelines to pull, clean, and validate market, logistics, inventory, and cost data (e.g., production, exports, sulfur inputs, regional demand) into our analytics environment. 
  • Document data lineage, metadata, and quality checks to ensure models are auditable and trustworthy.
• Feature engineering & modeling
  
  • Engineer features that capture illiquidity dynamics (sparse transactions, price gaps, seasonality), supply/demand shifts, and logistics constraints; test feature usefulness via cross validation and ablation studies.
  • Contribute to model selection/tuning (time series, probabilistic, gradient boosting, shallow neural nets) with rigorous backtesting and stress scenarios appropriate to fertilizer markets. 
• Model operations (MLOps)
  
  • Package and deploy models using our Dataiku environment, integrate with Snowflake data products, and build monitoring dashboards in Power BI; leverage Copilot Studio/ChatGPT
  • Enterprise for workflow accelerators and documentation. 
    Write clear model cards covering purpose, assumptions, data sources, performance, and limitations.
• Decision support
  
  • Translate model outputs into trader friendly visuals and narratives (marginal supplier/demand, price thresholds, scenario summaries).
  • Support weekly market reviews with timely updates and reproducible notebooks.

Minimum Qualifications (required)

• Working toward a Bachelor’s or Master’s in Data Science, Statistics, Applied Math, Computer Science, Economics/Finance, or related field.
• Coursework or projects in probability/statistics, time series, optimization, or econometrics.
• Practical experience with Python (pandas, numpy, scikit learn), SQL, and data visualization.
• Strong communication skills; ability to explain complex ideas simply to non technical stakeholders.

Additional Qualifications

• Exposure to commodity markets or agriculture; familiarity with fertilizer products (MAP/DAP/SSP/urea) and drivers (production, exports, input costs like sulfur). 
• Experience with Dataiku, Snowflake, Power BI, or similar tools for analytics engineering and dashboarding. 
• Interest in illiquid market modeling (intermittent price formation, wide bid ask, sparse transactions) and scenario/stress testing.
• Comfort with prompt engineering and AI documentation; ability to use enterprise LLM tools to accelerate analysis while maintaining governance.