Chao Lin Chang

• Promoted Agile and Scrum practices within the data team, coordinating GA4 and GTM tracking strategies with R&D and Operations to improve cross-team alignment and reporting efficiency.
• Automated CI/CD pipelines using GitHub Actions and Terraform, ensuring consistent deployment of analytics tracking and reducing manual configuration errors.

Key Project:

• Delivered GA4 and Looker Studio workshops for operations teams, increasing adoption of data-driven reporting, improving dashboard usage frequency, and strengthening self-service data capabilities across 6 operations teams.
• Migrated legacy ETL to modern ELT pipelines with Airbyte, dbt, and BigQuery, reducing cloud cost by approximately 40% and improved data consistency from 80% to nearly 100% .
• Co-led the implementation of a Customer Data Platform (CDP) with global vendors, achieving unified user data and streamlining campaign performance analysis across marketing channels.
• Built ML-based content tagging and a personalized recommendation engine (AWS Personalize), resulting in a 5–10% increase in CTR and improved user engagement across digital platforms.
• Led a cross-functional team on data-informed growth initiatives, leveraging Google Optimize and post-launch behavioral insights to run A/B tests and redesigns that boosted engagement and conversions 20–40%.

• Automated risk data aggregation and backtesting workflows in partnership with IT, cutting report generation time by 90% (from 2 hours to 12 minutes) and saved ~30 analyst hours per month.
• Built R-based predictive models to estimate default probabilities and distance-to-default for 800+ listed firms; risk signals were integrated into monthly investment committee reviews, resulting in 2 portfolio rebalancing decisions per quarter.

Key Project:

• Developed a SQL-based market risk framework to consolidate listed and OTC market data, enabling a unified daily reporting system adopted by 3 departments (risk, trading, and compliance), and reducing manual reconciliation time by 70%.
• Conducted regression analysis on 50+ long-short positions quarterly to evaluate hedging effectiveness, directly informing adjustments in proprietary trading strategy and reducing net exposure variance by 15%.