Srinath G.

- Led Product Development for the Experimentation Platform which allows users to set up of hundreds of A/B/n Tests across all Product lines (Food, Mart, Transport, Ads) to drive Data-driven decision making. Platform processed 25 billion events a day with high resiliency, uptime and automated scale up/down to ensure cost minimization.

- As a core member of Grab’s Auto Adaptive Systems workstream, enabled Machine Learning Experimentation and Feedback loops for Data Science teams to perform automated hyper parameter tuning via continuous experimentation.

- Built an Automated Experiment Analysis service to reduce effort spent by Analysts to analyze Experiment metrics by 80%. This was a big driver in increasing the velocity of Experiments run across Grab from 40 to 100 a month.

- Built a 0 to 1 API based Segmentation Platform allowing audience targeting for Marketing campaigns and Product Experiments.

- Expanded Experimentation Platform to Grab’s Digibank as a SaaS provider in a multi-tenant model to abide by the strict data segregation compliance regulations.

- Promoted in 12 months from Lead Product Manager to Senior Product Management Leader.

- Led 4 PMs (hired 3) and worked along side an Engineering team of 25 SDEs.

- Owned the Marketplace (3rd party Seller) Pricing strategy for Amazon Japan consisting of programs to (1) improve Price Competitiveness against key e-commerce players and retailers and (2) improving Amazon’s Price Perception.

- Launched a program to demote Uncompetitive Offers from Amazon’s BuyBox to Other Offers Listings page for all Amazon Japan 3rd party Seller Assortment, resulting in an improvement of Competitiveness by 673bps. Further built tools to surface these Uncompetitive offers via Seller Central and incentivized them to sign up for automatic price matching to Competitor by showing indicative lost Customer Views (translating to Revenue based on their CR)

- Built and fine tuned Machine Learning models to tackle Price Gouging by automatically removing egregiously priced Seller offers that impact Amazon’s Price Perception in JP. Model took inputs such as List Price, Average Selling price in last 6 months, Competitor price to determine egregious pricing. Removed 8.5M offers resulting in improvement of about 6PP in NPS

- Part of founding team to launch Amazon Prime Now in Singapore.

- Defined Pricing strategy for the entire local and import product offering including competitor benchmarking, systems setup, readiness and reporting.

- Delivered an improvement of 320bps of Pure Product Margin through changes in Pricing strategies defined based on multiple A/B tests and cohort analysis.