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Revanth kumar Gonuguntla

@revanthkumargonugunt

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Data Scientist at FHLB building fraud detection and compliance systems that cut losses and research time.

United States

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At FHLB, I architected a real-time fraud detection engine that combined gradient-boosted models with LLM-based transaction narrative analysis, saving $8M in annual losses and reducing false positives by 40%.

I also deployed a RAG-based regulatory compliance assistant over SEC, FINRA, and Basel III documentation, reducing manual research time by 60% while ensuring 100% audit accuracy. My work there also includes credit-risk scoring across a $2B loan portfolio.

Previously, at FMR, I used causal inference to evaluate marketplace and advertising changes. At TCS, I developed demand-forecasting models and an ML-based shopping and customer-service assistant.

Experience

Work history, roles, and key accomplishments

Current

Data Scientist

Current

FHLB

Jul 2025 - Present (1 year 3 months)

Architected a real-time fraud detection engine combining gradient-boosted models with LLM-based transaction narrative analysis, saving $8M in annual losses and reducing false positives by 40%. Deployed a RAG-based regulatory compliance assistant over SEC, FINRA, and Basel III documentation, reducing manual compliance research time by 60% while ensuring 100% audit accuracy.

Machine Learning Deep Learning LLM RAG Fraud Detection Gradient Boosting Compliance Credit Risk Python SQL

Data Scientist

FMR

Jun 2024 - May 2025 (11 months)

Improved marketplace matching efficiency by 15% by designing and implementing causal inference frameworks A/B testing, diff-in-diff to evaluate two-sided marketplace pricing and ranking changes. Mastered ad campaign ROI by 12% by building causal models to isolate true incremental impact of AdTech spend, controlling for confounding and selection bias.

Causal Inference A B Testing Difference In Differences Instrumental Variables Propensity Score Matching Python SQL AdTech Marketplace

Data Scientist

TCS

Jul 2021 - Aug 2023 (2 years 1 month)

Spearheaded demand-forecasting models integrating weather, promotions, and social signals, improving forecast accuracy by 30% and reduced stockouts by 25%. Developed an ML based shopping and customer-service assistant that autonomously handled returns, order tracking, and product queries, deflecting 65% of support tickets and saving $3M per year.

Demand Forecasting Machine Learning MLOps Python SQL Customer Service Automation