Daniel C.

Machine Learning Engineer @Wilson Sonsini Goodrich & Rosati

San Francisco, CA, US

ML Projects- Achieved 94% accuracy on operational strategy XGBoost model to classify attorney time-entry narratives- Collaborated with cross-functional teams and SMEs to correct mislabeled data; reduced error by 35%- Developed and executed training pipeline for explainable regression model to drive client intake strategy- Engineered prompts and tuned LLM parameters to reduce Azure OpenAI RAG hallucination by 30%.Data Management- Managed full stack of 12 dashboards: Python/SQL ingestion and cleaning, Azure CI/CD, PowerBI interface- Wrote, tested, and deployed 20 PySpark ETL scripts totaling lines- Designed and optimized ER models across 30+ sources using Power BI, reduced latency by up to 80%.

Stanford University

B.S., Mathematics - Minor in Music

Illinois Mathematics and Science Academy

High School Diploma

UC Berkeley School of Information

Master of Science - MS, Information and Data Science