Data Scientist with 4+ years of experience designing end-to-end AI solutions using Python, LangChain, MLflow, and AWS. Skilled in LLM, data pipelines, and predictive analytics to drive revenue optimization and customer e…
Data Scientist with 4+ years of experience designing end-to-end AI solutions using Python, LangChain, MLflow, and AWS. Skilled in LLM, data pipelines, and predictive analytics to drive revenue optimization and customer engagement. Passionate about leveraging ML and GenAI model development, from data engineering to deployment and storytelling.
Built end-to-end data pipelines for ingestion, preprocessing, and model execution, enabling near real-time demand prediction and reducing manual analysis effort by ~40%.
Designed and implemented machine learning-driven pricing models using Python, Databricks, and Snowflake to optimize product revenue, driving multi-million-dollar revenue lift and improved customer satisfaction.
Refactored legacy codebases and documented end-to-end workflows, improving performance, maintainability, and cross-team adoption.
Delivered executive-level insights using Snowflake and visual analytics.
Developed business rules and evaluation frameworks to test pricing recommendations, balancing revenue uplift with customer sensitivity to price changes.
Built end-to-end data pipelines for ingestion, preprocessing, and model execution, Designed and implemented machine learning–driven pricing models using Python, Databricks, and Snowflake, Refactored legacy codebases and documented end-to-end workflows, Developed business rules and evaluation frameworks to test pricing recommendations
Designed a clustering ML model-based school ranking system leveraging extended scoring rubrics across funding, governance, engagement, and quality dimensions to identify top 10 arts-focused schools.
Implemented a GenAI feedback engine using LLMs to automatically generate personalized principal reports.
Improved award decision-making while ensuring strong data security and privacy in alignment with privacy-preserving AI principles.
Designed a clustering ML model for school ranking system, Implemented a GenAI feedback engine implementing LLMs, Ensured strong data security and privacy in alignment with privacy-preserving AI principles
Designed and deployed a GenAI-powered Retrieval-Augmented Generation (RAG) chatbot using LangChain, Python, FastAPI, and AWS (Lambda, S3), boosting user engagement by 35%.
Built vector-based semantic search pipelines with FAISS and OpenAI embeddings, improving contextual accuracy and minimizing hallucination errors in enterprise knowledge retrieval.
Collaborated cross-functionally to define architecture.
Automated CI/CD with GitHub Actions.
Containerized deployments using Docker for version control.
Designed and deployed a GenAI-powered RAG chatbot using LangChain, Python, FastAPI, and AWS, Built vector-based semantic search pipelines with FAISS and OpenAI embeddings, Collaborated to define architecture, automate CI/CD with GitHub Actions, and containerize deployments using Docker
Engineered ELT (Extract, Load, Transform) processes and statistical analysis adopting Oracle SQL, contributing to a 20% reduction in data-related issues by ensuring efficient data flow and integrity.
Coordinated with cross-functional teams to implement Tableau dashboards for anomaly detection.
Used SQL and RPA (UiPath) for advanced data manipulation, leading to a 25% reduction in post-implementation defects.
Engineered ELT processes and Statistical Analysis adopting Oracle SQL, Coordinated with cross-functional teams to implement Tableau dashboards for anomaly detection
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