Premialab
Fintech Data Scientist
Led the cross-asset risk aggregation engine for QIS strategies — serving leading global investment banks and institutional clients managing US$20tn AUM with 60% faster, reliable pre-T+1 risk.
US$20tn
Client AUM served
60% ↓
Risk report runtime
90% ↓
Query & infra cost
What I worked on
- Led development of the cross-asset risk aggregation engine for QIS strategies, combining additive sensitivities (Greeks) and non-additive metrics (Historical VaR) — serving leading global investment banks and institutional clients managing US$20tn AUM with 60% faster reports and reliable pre-T+1 market-open risk.
- Collaborated with risk analysts and the CRO on risk validation, back-testing, and benchmarking for QIS derivative strategies.
- Designed and deployed a real-time anomaly-detection framework to monitor data quality and trigger escalation alerts, ensuring reliability across client-facing reports.
- Rebuilt data-extraction pipelines using asynchronous microservices, cutting processing time and infrastructure costs by 90%.
- Optimised SQL processes, cutting query time by 90%.
QISRisk EngineHistorical VaRMicroservicesSQL