Senior Data Scientist

Designed the data model and developed a multi-block pricing model for new customers across seven European countries, implemented a testing strategy, and created monitoring workflows, resulting in a 6.8% revenue increase and 60% warehouse adoption.

Designed and developed a rate increase model using survival models and customer lifetime value analysis, established a testing and monitoring strategy, increasing revenue from existing customers by 5% and applying to 80% of the customer base.

Developed ML models to forecast revenue for new warehouses and acquisitions, integrated an optimization tool for unit mix, and validated models, improving investment decisions and warehouse efficiency and facilitating tool adoption across locations.

Conducted in-depth analyses to uncover trends and provide data-driven recommendations to the Pricing Team and executives to support data-driven decision-making.

Created and enhanced tools for risk calculation and monitoring, produced monthly and quarterly risk reporting, and documented risk metrics production processes.

Worked on data reconciliation from different sources.

Provided performance analytics and communicated findings to investment country managers and fund trustees.

Performed due diligence and quantitative and qualitative analysis for fund and manager selections.

Provided investment-related studies for management and trustees, benchmarked pension fund performance and allocation, and supported reconciliation, verification, and communication of fund performances.

Designed and documented a method for quantifying operational risk (Advanced Approach) using R, conducted annual risk measurements and stress tests for regulatory purposes.

Developed operational risk maps for five new private equity firms and automated risk quantification processes.