Emmanuel Rajapandian — Data Scientist

Emmanuel is a Computer Engineer with a Master’s degree in Data Science from University of Texas at Austin. He currently serves as a Data Scientist II at Uber, within the APAC BizOps team. During his tenure as a Senior BIE at Amazon, Emmanuel drove projects converging Causal ML and GenAI to unlock over $225 million in incremental GMS. He engineered ML/LLM pipelines to extract attributes from customer anecdotes for ASIN enrichment, a key initiative that drove $50MM and improved conversion by 33 bps (from 3.26% to 3.59%). He architected a NL-to-SQL engine on AWS Athena and Bedrock, leveraging semantic parsing to achieve 96% accuracy in dynamic query generation. He also built an LLM-driven orchestrator using MCPs for intelligent agent routing, modernizing BI workflows. Beyond GenAI, Emmanuel built ensemble Predictive & CausalML frameworks that reduced Time to First Sale by 11 days, attributing 38% of gains to specific interventions, and led causal studies that identified a 37 bps conversion uplift from 3D enablement. Earlier in his career, Emmanuel led the development of ML-driven credit underwriting models at Applied Data Finance for PingTree loan channel, deploying ML models to rewrite Credit UW. He utilized gradient boosting techniques optimized via Optuna, to drive risk segmentation reducing delinquency by 20% and increasing monthly profits by $800K. He also acted as a technical POC to leadership, collaborating with Chief Analytics Officer to translate risk analytics into business strategies. His technical expertise spans Causal ML, Predictive Modelling, Forecasting, LLMs, MCPs, RAG pipelines, and LangChain. He is proficient in PyTorch, HuggingFace pipelines, and AWS tools including SageMaker, Glue, Athena, and Lambda, and adept in Python, SQL (PostgreSQL & SparkSQL) and Scala.

Stackforce AI infers this person is a Data Scientist specializing in SaaS and Fintech industries.

Experience: 4 yrs 11 mos