Angela Ramirez is a data engineer at Sam’s Club, where she works with the fraud team on one of retail’s highest-stakes machine learning problems: identifying fraudulent transactions at scale. With four years of experience as a data engineer, she specializes in machine learning for fraud prevention, designing and maintaining the data that powers models scoring activity in near real time and helping the business stop stolen-card transactions and return abuse before losses mount.

Her path into fraud detection builds on a strong applied foundation. Before Sam’s Club, she worked as a data engineer at Sephora, and her production toolkit spans feature pipelines that combine daily batches with real-time scoring, data modeling across relational, document, and graph databases including Elasticsearch, SPARQL, and Neo4j, and tooling such as PySpark, Cassandra, GCP and Dataproc, and data quality checks with Great Expectations. She also brings an MLOps perspective, covering model metrics, deployment, and monitoring.

Angela holds a master’s degree in Natural Language Processing, where she focused on coreference resolution and dialogue systems. Her most recent research explores generating quality synthetic data from LLMs for dialogue systems, connecting her academic background in language technologies with her day-to-day work building reliable data infrastructure.