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Data Scientist with 5+ years of experience building and deploying Machine Learning, Generative AI, and Agentic AI systems for real-world, high-scale environments. Expertise includes GPT-4/Claude, LLM-based RAG and semantic search, NLP, and model evaluation/monitoring to deliver grounded, reliable outputs. Delivered impactful AI solutions in financial services, including AI-powered automation, financial research copilot capabilities over millions of documents, and real-time inference pipelines on cloud-native architectures. Strong background in MLOps on AWS/GCP/Databricks and production-grade ML workflows.…Data Scientist with 5+ years of experience building and deploying Machine Learning, Generative AI, and Agentic AI systems for real-world, high-scale environments. Expertise includes GPT-4/Claude, LLM-based RAG and semantic search, NLP, and model evaluation/monitoring to deliver grounded, reliable outputs. Delivered impactful AI solutions in financial services, including AI-powered automation, financial research copilot capabilities over millions of documents, and real-time inference pipelines on cloud-native architectures. Strong background in MLOps on AWS/GCP/Databricks and production-grade ML workflows.

Chandu Mandalapu

Data Scientist, AI Engineer, Data Engineer, +2

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Data Scientist with 5+ years of experience building and deploying Machine Learning, Generative AI, and Agentic AI systems for real-world, high-scale environments. Expertise includes GPT-4/Claude, LLM-based RAG and semantic search, NLP, and model evaluation/monitoring to deliver grounded, reliable outputs. Delivered impactful AI solutions in financial services, including AI-powered automation, financial research copilot capabilities over millions of documents, and real-time inference pipelines on cloud-native architectures. Strong background in MLOps on AWS/GCP/Databricks and production-grade ML workflows.…Data Scientist with 5+ years of experience building and deploying Machine Learning, Generative AI, and Agentic AI systems for real-world, high-scale environments. Expertise includes GPT-4/Claude, LLM-based RAG and semantic search, NLP, and model evaluation/monitoring to deliver grounded, reliable outputs. Delivered impactful AI solutions in financial services, including AI-powered automation, financial research copilot capabilities over millions of documents, and real-time inference pipelines on cloud-native architectures. Strong background in MLOps on AWS/GCP/Databricks and production-grade ML workflows.

Available to hire

Data Scientist with 5+ years of experience building and deploying Machine Learning, Generative AI, and Agentic AI systems for real-world, high-scale environments. Expertise includes GPT-4/Claude, LLM-based RAG and semantic search, NLP, and model evaluation/monitoring to deliver grounded, reliable outputs.

Delivered impactful AI solutions in financial services, including AI-powered automation, financial research copilot capabilities over millions of documents, and real-time inference pipelines on cloud-native architectures. Strong background in MLOps on AWS/GCP/Databricks and production-grade ML workflows.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Data Scientist at JPMorgan Chase & Co.

September 1, 2024 - Present

Architected a GenAI-powered financial research copilot using GPT-4, Claude, RAG, and vector search to enable analysts to query 5M+ pages of research reports, earnings transcripts, and regulatory filings via natural language. Built a document intelligence pipeline using LLMs/NLP and semantic chunking to extract key financial insights from 250K+ annual reports, earnings releases, and SEC filings. Designed and optimized retrieval-augmented generation (RAG) frameworks with embeddings and vector databases to improve grounding and response relevance. Implemented an agentic workflow to autonomously retrieve, analyze, and synthesize information across multiple financial sources, generating explainable summaries for decision support. Added LLM evaluation and monitoring with automated benchmarking and hallucination detection, improving factual consistency by 28%. Developed scalable inference/orchestration pipelines on AWS for low-latency processing of thousands of daily queries.

Data Scientist at LTIMindtree

February 1, 2020 - August 31, 2023

Built machine learning credit risk prediction models using Python, SQL, XGBoost, LightGBM, and Random Forest on 10M+ loan/customer records to improve default prediction accuracy by 24%. Engineered scalable feature pipelines with PySpark and SQL to create 300+ risk indicators from transaction history, credit utilization, repayment patterns, and demographics, improving performance by 18%. Implemented explainability using SHAP and feature importance to support regulatory compliance and transparent risk attribution. Developed AWS SageMaker-based ML/DL pipelines with hyperparameter tuning and ensembling, reducing training time by 40%. Designed data engineering pipelines using Apache Spark and Kafka with AWS services (S3, Glue, Athena) for real-time analytics consumption. Applied statistical analysis, hypothesis testing, and A/B experimentation to improve approval-to-default tradeoffs and reduce portfolio risk exposure by 15%. Established MLOps practices on AWS SageMaker/Databricks including