Owns data pipeline integrity, validation, reliability, and monitoring. Designs data quality frameworks and automated testing, investigates infrastructure issues, validates vendor data, documents quality controls, and supports product launches. The role collaborates with engineering, product, quantitative modeling, solutions, and external data vendors to ensure data is accurate, timely, fit for purpose, and launch-ready.

Build and scale production Python pipelines for economic and financial climate-risk models. Responsibilities include transforming research prototypes into tested infrastructure, supporting statistical, econometric, and machine-learning modeling, cleaning large financial and hazard datasets, resolving data anomalies, and collaborating with engineering, product, and client teams to document outputs and communicate findings.