Javier

Researched and developed time series modeling, forecasting, and anomaly detection. Helped with BAU duties within the existing forecasting framework and contributed with the development of different PoCs, including time series modeling techniques (Econometrics and ML), PySpark, Hive, and Python within a mature Jenkins CI/CD framework.

Researched and developed proof of concept for clinical study feasibility platform. ML techniques such as time series modeling, NLP, Deep Learning Transformers, and Ensemble models were used to build outperforming predictive models. Also led the Data Science team through different stages of research, development, and productionizing ML predictive models as products, while also introducing industrial coding standards within a robust CI/CD environment.

Worked on risk assessment, compliance, and anomaly detection models based on voice and language data from financial institutions. Helped the business provide their clients with a robust anomaly detection framework by implementing new predictive models and contributing to continuous development and innovation of RecordSure’s voice analytics platform.