Industry
Production Data Science & Anomaly Detection
Built data pipelines and predictive systems for behavioral analytics, anomaly detection, and operational monitoring. Worked across ETL, feature engineering, predictive modeling, and production analytics.
This work centered on production data science systems for behavioral analytics, anomaly detection, and operational monitoring. The focus was turning raw, heterogeneous data into reliable signals that could support day-to-day decisions.
Data and feature pipelines
I worked across ETL, data validation, and feature engineering to create analysis-ready datasets for downstream models and monitoring. These pipelines provided a consistent foundation for both exploratory analysis and recurring production workflows.
Predictive analytics
I developed predictive approaches for behavioral analysis and anomaly detection, translating observed patterns into indicators that could be tracked over time. The work included model development as well as the surrounding analytics needed to interpret outputs in an operational context.
Operational monitoring
The final layer connected model and pipeline outputs to production analytics, making unusual behavior and changes in key signals visible for investigation.