Context
At Bayer, I interned with the Solutions team under Engineering and Data Science, working on machine learning, data engineering, and operational analytics for seed processing and field equipment operations.
What I Built
I built XGBoost and LightGBM regression models to predict machine-level seed processing times from historical throughput and runtime data, achieving RMSE = 35. These models supported capacity planning and maintenance scheduling for upcoming planting cycles.
I also engineered ETL pipelines over 10GB+ of agronomic and equipment data from BigQuery and SQL Server, consolidating multi-year data from multiple sources and reducing retrieval latency by 40%.
Challenges
The work involved fragmented operational data across systems, inconsistent data collection practices, and the need to make equipment breakdown trends more visible across global sites. I analyzed breakdown patterns across 21 locations and partnered with demand planning, engineering, and automation support teams to refine data collection strategies.
Impact & Takeaways
I developed Tableau dashboards for downtime analysis that supported a 30% reduction in failures across 15+ critical field assets. This experience strengthened my skills in Python, SQL, BigQuery, machine learning, ETL design, Tableau, and cross-functional analytics while showing me how data science can improve operational planning and engineering decision-making.