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Senior Data Scientist with 6+ years designing and deploying AI, machine learning, deep learning, and generative AI solutions across healthcare, automotive, fintech, and financial services. Skilled in statistical inference, hypothesis testing, A/B testing, and experiment design, turning quantitative analysis into production-ready predictive models and enterprise RAG/LLM applications. Hands-on across the full stack—Python, SQL, Spark, TensorFlow/PyTorch, LangChain, and cloud-native MLOps—building RAG systems, fine-tuning LLMs with LoRA/QLoRA, deploying models with AWS SageMaker/Azure ML/Vertex AI, and delivering insights through BI dashboards. Experienced in responsible AI, model governance, explainability (SHAP/LIME), and production monitoring.…Senior Data Scientist with 6+ years designing and deploying AI, machine learning, deep learning, and generative AI solutions across healthcare, automotive, fintech, and financial services. Skilled in statistical inference, hypothesis testing, A/B testing, and experiment design, turning quantitative analysis into production-ready predictive models and enterprise RAG/LLM applications. Hands-on across the full stack—Python, SQL, Spark, TensorFlow/PyTorch, LangChain, and cloud-native MLOps—building RAG systems, fine-tuning LLMs with LoRA/QLoRA, deploying models with AWS SageMaker/Azure ML/Vertex AI, and delivering insights through BI dashboards. Experienced in responsible AI, model governance, explainability (SHAP/LIME), and production monitoring.

SRI DATTA NADIPOLLA

Data Scientist, AI Engineer, Data Engineer, +2

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Senior Data Scientist with 6+ years designing and deploying AI, machine learning, deep learning, and generative AI solutions across healthcare, automotive, fintech, and financial services. Skilled in statistical inference, hypothesis testing, A/B testing, and experiment design, turning quantitative analysis into production-ready predictive models and enterprise RAG/LLM applications. Hands-on across the full stack—Python, SQL, Spark, TensorFlow/PyTorch, LangChain, and cloud-native MLOps—building RAG systems, fine-tuning LLMs with LoRA/QLoRA, deploying models with AWS SageMaker/Azure ML/Vertex AI, and delivering insights through BI dashboards. Experienced in responsible AI, model governance, explainability (SHAP/LIME), and production monitoring.…Senior Data Scientist with 6+ years designing and deploying AI, machine learning, deep learning, and generative AI solutions across healthcare, automotive, fintech, and financial services. Skilled in statistical inference, hypothesis testing, A/B testing, and experiment design, turning quantitative analysis into production-ready predictive models and enterprise RAG/LLM applications. Hands-on across the full stack—Python, SQL, Spark, TensorFlow/PyTorch, LangChain, and cloud-native MLOps—building RAG systems, fine-tuning LLMs with LoRA/QLoRA, deploying models with AWS SageMaker/Azure ML/Vertex AI, and delivering insights through BI dashboards. Experienced in responsible AI, model governance, explainability (SHAP/LIME), and production monitoring.

Available to hire

Senior Data Scientist with 6+ years designing and deploying AI, machine learning, deep learning, and generative AI solutions across healthcare, automotive, fintech, and financial services. Skilled in statistical inference, hypothesis testing, A/B testing, and experiment design, turning quantitative analysis into production-ready predictive models and enterprise RAG/LLM applications.

Hands-on across the full stack—Python, SQL, Spark, TensorFlow/PyTorch, LangChain, and cloud-native MLOps—building RAG systems, fine-tuning LLMs with LoRA/QLoRA, deploying models with AWS SageMaker/Azure ML/Vertex AI, and delivering insights through BI dashboards. Experienced in responsible AI, model governance, explainability (SHAP/LIME), and production monitoring.

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Work Experience

Senior Data Scientist at McKesson

September 1, 2025 - Present

Lead generative AI and predictive analytics initiatives for enterprise healthcare operations. Built production-ready AI assistants and ML models under model-governance requirements; reduced manual processing time by 40% using predictive models, LLM applications, and RAG pipelines. Improved model performance by 18% via feature engineering, statistical analysis, and hyperparameter tuning for fraud and risk prediction. Accelerated knowledge retrieval using hybrid semantic search with Pinecone/Weaviate/ChromaDB and reduced document-lookup time. Implemented ongoing model monitoring and error analysis to catch performance drift early. Delivered executive visibility through dashboards and technical documentation.

Data Scientist at Mitsubishi Motors

July 1, 2024 - August 31, 2025

Owned forecasting, personalization, and computer-vision initiatives for automotive sales and quality inspection from data pipeline design through production deployment. Improved demand forecasting accuracy using Python, PySpark, TensorFlow, XGBoost, and LightGBM. Reduced pipeline refresh latency by designing scalable Spark/Databricks pipelines integrating Snowflake, PostgreSQL, MongoDB, and AWS SageMaker. Built retrieval-based recommendation systems with Hugging Face embeddings and vector search. Increased defect-detection precision using computer vision (OpenCV, PyTorch) for damage detection and image classification. Implemented MLOps pipelines (MLflow, Docker, Kubernetes, CI/CD) with automated model monitoring.

Data Scientist at Poonawalla Fincorp Limited

February 1, 2022 - April 30, 2024

Built credit-risk and fraud models supporting regulated lending decisions in collaboration with risk and compliance teams. Improved lending decision accuracy by 15% using credit-risk, loan-default, and fraud-detection models with Python, Spark, and scikit-learn, validated through statistical inference and hypothesis testing. Increased ETL throughput by building distributed pipelines in Spark/SQL and AWS. Strengthened generalization using controlled experiments and cross-validation. Shortened executive reporting cycles via Tableau dashboards for loan performance and delinquency. Supported audit readiness by deploying production-ready models with clear technical documentation.

Data Scientist at Paytm Payment Services

March 1, 2020 - January 31, 2022

Delivered fraud-detection, risk-scoring, and customer-analytics models for a large-scale digital payments platform. Increased fraud-model accuracy by 15% using benchmarked algorithms (e.g., XGBoost, Random Forest, scikit-learn). Enabled large-scale transaction analytics with Spark, Hadoop, and SQL. Reduced confirmed fraud cases by building predictive risk-scoring and customer lifetime value models grounded in statistical inference. Improved validation through exploratory analysis, hypothesis testing, and hyperparameter optimization. Developed NLP models for text classification and sentiment analysis and performed customer segmentation (K-Means and hierarchical clustering). Automated reporting workflows using Python scripting, SQL procedures, and scheduled notebooks.