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prajval shet

@prajvalshet

Message

Senior Decision Scientist building ML models that drive business cost savings.

United States

Message

What I'm looking for

I’m looking to lead impactful analytics and ML work—shipping real-time models, improving fraud/risk outcomes, and collaborating cross-functionally to turn data into measurable business results and cost savings.

I’m a Senior Analytics/Decision Scientist with 8 years of experience turning statistical modeling and machine learning into business decisions across retail, telecom, and finance.

As AI & Decision Science Lead at London Stock Exchange Group, I lead data extraction, feature evaluation, model validation, and real-time analytics deployment in Azure ML—generating $5M+ in cost savings. I’ve also designed an Account Insights scoring framework, used FAISS for approximate nearest-neighbor similarity search, and built a GAN-based synthetic fraud data system to improve detection of rare and unseen patterns.

Previously at Corvia, I deployed predictive risk models using machine learning and implemented multivariate anomaly detection with Isolation Forest, cutting annual overhead cost by 34%. Earlier at Verizon, I performed K-means clustering and cohort analysis for churn insights and built ANN-based network failure prediction with 85% accuracy for rarely occurring events, partnering across product and technical teams to deliver end-to-end model development.

Experience

Work history, roles, and key accomplishments

Current

AI & Decision Science Lead

Current

London Stock Exchange Group

Oct 2023 - Present (2 years 7 months)

Led data extraction, feature evaluation, and model validation in Azure ML to deliver real-time analytics solutions that generated $5M+ in cost savings. Built fraud detection capabilities using FAISS similarity search and a GAN-based synthetic fraud data system to improve detection of rare and unseen patterns.

Python SQL Data Mining ETL Machine Learning Feature Engineering Faiss Fraud Detection

Machine Learning Sr Data Scientist

Corvia

Sep 2022 - Jul 2023 (10 months)

Extracted and assessed data in BigQuery to support product development and deployed predictive risk models using machine learning. Implemented multivariate anomaly detection with Isolation Forest to detect anomalous merchant behavior and reduce annual overhead cost by 34%.

BigQuery Machine Learning predictive modeling Isolation Forest Anomaly Detection Data Extraction SQL

Sr Data Scientist

Michaels

Nov 2021 - Sep 2022 (10 months)

Compared store and online recommendations using Distributed (ATD) and Repeat customer probability (RCP) models. Developed a real-time recommendation engine using Alternating Least Squares and Deep Boltzmann machines, and improved recommendations via competitor web scraping, increasing click-through rate by 15%.

Recommendation Systems Distributed (ATD) Modeling Repeat Customer Probability (RCP)Alternating Least Squares Deep Boltzmann Machines Web Scraping

Data Architect/Data Scientist

Verizon

Jun 2018 - Sep 2021 (3 years 3 months)

Performed clustering and cohort analysis with K-means to identify customer churn drivers and used RFM-style metrics to derive customer lifetime value. Designed ML to predict network cord failures using artificial neural networks, achieving 85% accuracy for rare events and collaborating with product and technical teams to deliver models end to end.

Cohort Analysis Churn Modeling Neural Networks predictive modeling Data Modeling SQL