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Senior Data Scientist and AI/ML Engineer with 11+ years of IT experience, specializing in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and production ML solutions. Strong expertise in Python, SQL, PySpark, Databricks, Azure/AWS/GCP, LangChain, LangGraph, FastAPI, MLflow, Docker, Kubernetes, and MLOps. Experienced in building scalable ML pipelines, predictive models, AI agents, and enterprise-grade AI applications from development through production. Proven ability to translate complex business requirements into reliable, data-driven solutions and collaborate effectively with technical and business stakeholders.…Senior Data Scientist and AI/ML Engineer with 11+ years of IT experience, specializing in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and production ML solutions. Strong expertise in Python, SQL, PySpark, Databricks, Azure/AWS/GCP, LangChain, LangGraph, FastAPI, MLflow, Docker, Kubernetes, and MLOps. Experienced in building scalable ML pipelines, predictive models, AI agents, and enterprise-grade AI applications from development through production. Proven ability to translate complex business requirements into reliable, data-driven solutions and collaborate effectively with technical and business stakeholders.

RANI SUPRIYA

Data Scientist, AI Engineer, Data Engineer, +1

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Senior Data Scientist and AI/ML Engineer with 11+ years of IT experience, specializing in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and production ML solutions. Strong expertise in Python, SQL, PySpark, Databricks, Azure/AWS/GCP, LangChain, LangGraph, FastAPI, MLflow, Docker, Kubernetes, and MLOps. Experienced in building scalable ML pipelines, predictive models, AI agents, and enterprise-grade AI applications from development through production. Proven ability to translate complex business requirements into reliable, data-driven solutions and collaborate effectively with technical and business stakeholders.…Senior Data Scientist and AI/ML Engineer with 11+ years of IT experience, specializing in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and production ML solutions. Strong expertise in Python, SQL, PySpark, Databricks, Azure/AWS/GCP, LangChain, LangGraph, FastAPI, MLflow, Docker, Kubernetes, and MLOps. Experienced in building scalable ML pipelines, predictive models, AI agents, and enterprise-grade AI applications from development through production. Proven ability to translate complex business requirements into reliable, data-driven solutions and collaborate effectively with technical and business stakeholders.

Available to hire

Senior Data Scientist and AI/ML Engineer with 11+ years of IT experience, specializing in Machine Learning, Generative AI, LLMs, RAG, Agentic AI, and production ML solutions. Strong expertise in Python, SQL, PySpark, Databricks, Azure/AWS/GCP, LangChain, LangGraph, FastAPI, MLflow, Docker, Kubernetes, and MLOps. Experienced in building scalable ML pipelines, predictive models, AI agents, and enterprise-grade AI applications from development through production. Proven ability to translate complex business requirements into reliable, data-driven solutions and collaborate effectively with technical and business stakeholders.

Skills

Experience Level

Expert

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Expert

Work Experience

Senior Data Scientist at Citi Bank

October 1, 2024 - Present

Architected enterprise-scale AI/ML and Generative AI solutions for fraud detection, risk analytics, and customer intelligence. Built real-time fraud detection pipelines using Apache Spark Structured Streaming and Kafka with AWS SageMaker and Snowflake, processing 6M+ transactions daily. Developed RAG and semantic search solutions using OpenAI GPT-4/4o, LangChain/LangGraph, Hugging Face, FAISS/Pinecone, and prompt engineering for intelligent financial assistants. Implemented scalable feature engineering and data preparation using PySpark, Pandas, SQL, and Airflow. Delivered production-ready MLOps using MLflow, Kubeflow, Docker, Kubernetes, Jenkins, GitHub Actions, and Feature Store (Feast) with monitoring, drift detection, rollback, and retraining. Exposed secure RESTful inference services using FastAPI/Flask, OAuth 2.0, Docker/Kubernetes, and API specs. Built dashboards and AI monitoring with Power BI/Tableau/Grafana/Prometheus and maintained 99.9% availability through incident respons

Senior Data Scientist / ML Engineer at CVS Health

June 1, 2022 - September 30, 2024

Designed and developed enterprise ML systems for healthcare analytics including predictive modeling for patient risk stratification, disease prediction, and readmission forecasting. Built scalable ETL and feature engineering pipelines (PySpark, Airflow, Snowflake, Redshift, AWS Glue) on 10TB+ healthcare data. Developed Generative AI solutions with OpenAI GPT-4, LangChain, Hugging Face, RAG, FAISS/Pinecone for clinical document search and summarization. Implemented healthcare NLP (BERT/BioBERT/RoBERTa, NER, sentence transformers) and deployed MLOps pipelines using MLflow, Kubeflow, Docker, Kubernetes, Jenkins, GitHub Actions, CI/CD, and Feast. Built secure low-latency ML APIs with FastAPI/Flask/OAuth 2.0 and production streaming analytics via Kafka and Spark Structured Streaming. Optimized distributed training with Spark and GPU acceleration; implemented XAI via SHAP/LIME and ensured responsible AI practices.

Data Scientist at Wells Fargo

July 1, 2021 - May 31, 2022

Built end-to-end ML solutions for customer analytics, fraud prevention, and credit risk modeling. Developed predictive models for churn, loan default forecasting, fraud detection, and CLV using scikit-learn, TensorFlow, PyTorch, XGBoost/LightGBM, and PySpark. Engineered scalable ETL/feature engineering pipelines using PySpark, AWS Glue, Snowflake, Redshift, and Airflow. Delivered NLP solutions using BERT/RoBERTa and Hugging Face (classification, sentiment, NER, document intelligence) and implemented Generative AI POCs with OpenAI GPT APIs, RAG, FAISS/Pinecone. Produced secure RESTful ML inference services using FastAPI/Flask, Docker, Kubernetes, and OAuth 2.0. Implemented MLOps with MLflow, Docker, Kubernetes, Jenkins, and CI/CD; added governance and explainability with SHAP/LIME and monitoring.

Data Scientist at eBay

June 1, 2017 - June 30, 2021

Developed scalable ML solutions for e-commerce personalization and recommendation systems. Built recommendation engines using collaborative filtering, matrix factorization, deep learning (TFRS) and PyTorch/TensorFlow approaches, improving conversion by 22%. Implemented NLP for product search and review understanding using BERT/RoBERTa and embedding models, including NER and sentiment analysis. Created large-scale ETL and feature pipelines with PySpark, Spark SQL, Hive, Snowflake, and Airflow. Built customer segmentation and behavioral analytics models using clustering and dimensionality reduction; implemented streaming analytics with Kafka and Spark Structured Streaming. Developed forecasting and CLV models using XGBoost/LightGBM and time-series methods (Prophet, TensorFlow/PyTorch). Deployed via cloud-native architectures and automated MLOps with MLflow, Docker, Jenkins, and Kubernetes; added explainability with SHAP/LIME and built executive dashboards using Tableau/Power BI/Grafana.

Junior Data Analyst → Data Scientist at Searchlight India Ltd

July 1, 2014 - December 31, 2016

Contributed to enterprise analytics by transforming raw data into actionable insights. Built dashboards and reports using Tableau/Power BI/SQL and visualization libraries. Optimized SQL queries, views, joins, CTEs, and database objects across MySQL/PostgreSQL/SQL Server, reducing report generation time by 35%. Developed predictive models for segmentation, sales forecasting, and operational optimization using regression, classification, trees, and clustering. Built automated preprocessing and feature engineering workflows (Python/Pandas/NumPy/SQL) and performed EDA and statistical modeling with SciPy/Statsmodels. Designed ETL workflows for data ingestion from CSV/Excel/APIs/flat files and supported data validation/quality checks. Provided production support for ETL jobs and analytics environments, improving reliability and reducing processing time by 30%.