Welcome to my page! I'm Shriya, a Data enthusiast from Boston, USA. I specialise in fraud and risk analytics, with deep experience detecting synthetic identity fraud, first-party fraud, and chargebacks across fintech and eCommerce. I’ve built and deployed ML models (XGBoost, calibrated with isotonic regression), engineered high-signal features (PII overlaps, IP/device velocity, inquiry bursts), and used SHAP for interpretability and threshold tuning tailored to bank-specific risk profiles.

I've led investigations using Neo4j to map fraud rings, integrated vendor breach/IP signals, and worked closely with fraud ops, engineering, and stakeholders to operationalise fraud detection at scale. Outside of fraud, I’ve also worked on digital analytics use cases, including A/B testing, customer journey analysis, and e-commerce performance insights.

Outside of work, you will find me creating travel content that reaches 2M+ viewers monthly across Instagram and TikTok. Its a creative outlet that fuels my love for storytelling and exploration!

Tech Stack Python, SQL, Statistical Analysis, Machine Learning, SHAP, Graph DB - Neo4j, Airflow, Snowflake, BigQuery, Redshift, Adobe Analytics, BI Tools (Power BI, Tableau, StreamLit), Excel, Git, AWS, Gen AI