ML Investment & Analytics Products
Forecasting and classification products for APAC financial clients — including an alternative-data investment strategy — on a Big Data platform whose adoption doubled.
- ~30%above benchmark
- 2×platform adoption
- 40%less processing time
Architecture · simplified
- Market & alt dataFinancial and alternative datasets
- Data platformAWS EMR, Glue, Redshift
- ModelsForecasting, gradient boosting
- ProductsSignals & analytics for clients
Context
Financial clients across APAC wanted more than raw data — they wanted analytics and signals they could act on, tailored to how they invest.
The challenge
Turn large, messy financial and alternative datasets into products that beat benchmarks, and make the underlying platform fast and easy enough that clients actually adopt it.
What I did
- Owned the roadmap and delivery of a Big Data analytics platform for APAC financial clients, tailoring analytics and ML solutions to client needs.
- Built ML-powered investment and analytics products, including a rotation investment strategy using alternative data and gradient-boosting models.
- Designed scalable data and ML systems on AWS (EMR, Glue, Redshift) to speed up analytics workflows.
Outcome
Models outperformed benchmarks by ~30%, platform adoption doubled, and data processing time fell by 40% — directly shaping product strategy.