B ao Yin g Wong

Architected and delivered a Client Manager Productivity Optimization model (end-to-end analytics), covering data ingestion, pipeline design, Dataiku modeling, and senior management reporting, serving 10+ stakeholders across business units. Built end-to-end pipelines by consolidating fragmented multi-source datasets using Power Query/Excel, transforming raw operational data into structured reusable reporting layers with consistent lineage. Implemented optimization logic to quantify task effort allocation for a newly established client manager function, improving workforce capacity planning and resource forecasting accuracy. Developed interactive dashboards to surface productivity drivers, task distribution metrics, and optimization signals for C-suite visibility. Automated recurring reporting workflows, reducing manual processing time by ~30% while improving data consistency across business units. Bridged business and technical stakeholders through requirements gathering, solution desig

Designed and maintained analytical data pipelines in Snowflake SQL for sourcing, transformation, cleaning, and structuring data for downstream predictive models and client-facing reporting. Built Python-based time-series forecasting and customer segmentation models using techniques such as cyclic pattern analysis, behavioral segmentation, and correlation modeling. Developed modular analytics workflows that connected raw data sources through transformation layers to predictive model outputs, following dbt-style layered data modeling principles. Created interactive cashflow and scenario planning models enabling clients to stress-test assumptions and observe downstream financial impacts in near real time. Automated ad hoc reporting pipelines and data extraction tasks, reducing analyst turnaround time by ~35% on recurring deliverables. Presented model findings and data-driven recommendations to senior management and clients, translating complex analytics into business decisions. Mentored j

Developed and maintained actuarial models with structured data inputs and automated processing logic to support regulatory and internal reporting requirements. Migrated legacy SQL-based data processes into Alterxyx workflows, improving pipeline reliability, execution speed, and data preparation consistency. Automated recurring data transformations across reporting, modeling, and regulatory submission pipelines, reducing manual intervention and error risk.