Building scalable, intelligent, and high-performance ML solutions
Data Scientist with 4+ years of experience designing and deploying machine learning and AI solutions across fintech and enterprise. Skilled in real-time fraud detection, NLP, graph-based risk modeling, and cloud-native ML platforms.
I'm a Data Scientist & AI Engineer with 4+ years of experience designing and deploying machine learning solutions across fintech and enterprise domains.
At PayPal, I engineer real-time fraud detection pipelines processing 20–30M daily transactions using PySpark, Kafka, and Graph Neural Networks — achieving sub-120ms inference latency at 99.9% uptime.
I thrive at the intersection of research and production: from training transformer models and building RAG pipelines to designing the data infrastructure that powers real-time decisions at scale.
Engineered fraud detection pipelines processing 20–30M daily transactions. Used GraphSAGE/GAT to detect fraud rings, improving network-level recall by 14%. Hybrid GNN + transformer + RAG ensemble lifted AUC from 0.91 to 0.96.
Built enterprise lakehouse on Azure Data Lake Gen2 + Snowflake, consolidating 40+ sources. Cut retrieval time by 35%, improved data accuracy from 89% to 97%.
NLP-driven risk monitoring ingesting news and filings. FAISS + LangChain RAG pipeline for contextual Q&A over supplier documents with structured risk scoring.
ML pipeline with XGBoost and LSTM models using rolling averages, seasonal trends, and lag features. Improved forecast accuracy by 12% with cross-validation tuning.
Kafka + Spark Structured Streaming pipelines processing 8M daily events. Real-time anomaly detection cut incident response time by 28% via automated monitoring.
Open to data science, ML engineering, and AI research roles. Based in NJ — open to relocate. Always happy to talk data, fraud systems, or interesting ML problems.