Developed and piloted customer churn prediction models using XGBoost, Spark SQL, and Python; improved targeting precision by 6.2% and reduced false positives by 3.8%; built an initial CLV model using Ridge, Lasso, and XGBoost; designed a proof-of-concept recommender system on AWS EMR; implemented NLP and RAG pipelines (FAISS + LLMs) to analyze customer feedback; automated ETL validation and reporting workflows, reducing manual checks by 18% and data preparation time by 14%.

AI R&D Research Intern at Walgreens

September 1, 2024 - July 1, 2025

Developed an NLP-based sentiment analysis model on Walgreens retail and pharmacy feedback; built a text summarization pipeline; designed an NER model extracting 18,000+ entities; delivered applied NLP solutions including a customer complaint classifier (91.8% precision) and a RAG-based knowledge retrieval system.

Data Scientist at Kodal India

August 1, 2022 - August 1, 2023

Built machine learning models for credit risk assessment, customer acquisition, and fraud detection; achieved a 15% reduction in default rates, a 12% increase in customer conversions, and a 25% decrease in fraudulent transactions through Python, TensorFlow, scikit-learn, and AI-driven algorithms; created automated feature engineering pipelines, reducing model training time by 30%; led large-scale data analysis using Hadoop, Spark, and NLP techniques; implemented real-time data streaming solutions with Apache Kafka and AWS Kinesis for credit scoring, and utilized time series analysis to forecast financial trends, contributing to a 5% increase in portfolio returns.