Pittsburgh, PA
Machine Learning Engineer and Carnegie Mellon M.S. candidate with experience building and evaluating ML systems across ad-tech, financial data, and health research. Delivered measurable gains in advertiser gAUC (+0.0095), calibration (98% lower ECE), and pipeline reliability (99.6% nightly success) using Python, PyTorch, Databricks, PySpark, and SQL. Published researcher combining multi-task modeling, scalable ETL, and rigorous offline experimentation.
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