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I am Umasankar Rumasankare, a data science professional with 10+ years of experience delivering enterprise-scale AI, machine learning, and advanced analytics solutions that drive strategic business outcomes. I specialize in architecting production-grade ML and Generative AI systems, with a focus on LLMs, RAG, and agentic frameworks to build intelligent, context-aware applications. I thrive on turning complex datasets into actionable insights, building scalable data pipelines in cloud environments, and advancing responsible AI through governance and robust testing.…I am Umasankar Rumasankare, a data science professional with 10+ years of experience delivering enterprise-scale AI, machine learning, and advanced analytics solutions that drive strategic business outcomes. I specialize in architecting production-grade ML and Generative AI systems, with a focus on LLMs, RAG, and agentic frameworks to build intelligent, context-aware applications. I thrive on turning complex datasets into actionable insights, building scalable data pipelines in cloud environments, and advancing responsible AI through governance and robust testing.

Umasankar

Data Scientist, AI Engineer, AI Developer, +3

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I am Umasankar Rumasankare, a data science professional with 10+ years of experience delivering enterprise-scale AI, machine learning, and advanced analytics solutions that drive strategic business outcomes. I specialize in architecting production-grade ML and Generative AI systems, with a focus on LLMs, RAG, and agentic frameworks to build intelligent, context-aware applications. I thrive on turning complex datasets into actionable insights, building scalable data pipelines in cloud environments, and advancing responsible AI through governance and robust testing.…I am Umasankar Rumasankare, a data science professional with 10+ years of experience delivering enterprise-scale AI, machine learning, and advanced analytics solutions that drive strategic business outcomes. I specialize in architecting production-grade ML and Generative AI systems, with a focus on LLMs, RAG, and agentic frameworks to build intelligent, context-aware applications. I thrive on turning complex datasets into actionable insights, building scalable data pipelines in cloud environments, and advancing responsible AI through governance and robust testing.

Available to hire

I am Umasankar Rumasankare, a data science professional with 10+ years of experience delivering enterprise-scale AI, machine learning, and advanced analytics solutions that drive strategic business outcomes.

I specialize in architecting production-grade ML and Generative AI systems, with a focus on LLMs, RAG, and agentic frameworks to build intelligent, context-aware applications. I thrive on turning complex datasets into actionable insights, building scalable data pipelines in cloud environments, and advancing responsible AI through governance and robust testing.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior Data Scientist – AI/ML & Generative AI at JPMorgan Chase

February 1, 2023 - Present

Designed and deployed end-to-end Machine Learning and Generative AI solutions to solve complex business problems and improve decision-making. Architected LLM-powered applications using RAG for context-aware search and conversational AI. Fine-tuned foundation models with LoRA/QLoRA to boost domain accuracy and reduce costs. Built scalable ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment. Developed vector search for semantic retrieval, implemented MLOps/LLMOps with MLflow, Airflow, and Docker, and deployed containerized workloads on Kubernetes across cloud platforms. Led data-processing collaborations with engineers using Spark, designed real-time and batch inference pipelines, and applied advanced statistical methods to derive actionable insights. Improved model performance via feature engineering and cross-validation; created RESTful APIs and monitoring to ensure reliability and governance.

Senior Data Scientist – AI/ML Roles & Responsibilities at Stripe

August 1, 2021 - January 1, 2023

Developed and deployed ML models for customer segmentation, churn prediction, fraud detection, and demand forecasting. Built end-to-end ML workflows from data collection to production deployment, including NLP-based text analytics and time-series forecasting. Implemented scalable data pipelines with Spark, and delivered dashboards for stakeholder communication. Optimized models through hyperparameter tuning, A/B testing, and robust validation, and deployed models via REST APIs for real-time inference on cloud platforms.

Senior Data Scientist – AI/ML at Walmart

February 1, 2019 - July 1, 2021

Built ML models for customer analytics, sales forecasting, and risk modeling. Conducted EDA, engineered predictive features, and deployed end-to-end ML workflows. Developed NLP solutions for text analytics and sentiment classification, and created time-series models for operational planning. Collaborated with data engineers to optimize data pipelines and delivered interactive dashboards for stakeholders.

Data Scientist – Machine Learning at UnitedHealth

August 1, 2016 - January 1, 2019

Supported development of ML models to analyze healthcare data, performed data cleaning and preprocessing, and conducted EDA to identify trends and data quality issues. Implemented basic supervised learning algorithms, supported feature engineering, and automated reporting. Contributed to model validation, experimentation, and deployment activities within a cross-functional team.

ML Engineer / Data Analyst at XPO Logistics

February 1, 2014 - July 1, 2016

Collected and analyzed large datasets to support BI and operational decision-making. Developed SQL queries, automated reports, and built dashboards to track KPIs. Assisted in developing early-stage ML models for predictive analytics and collaborated with cross-functional teams to integrate analytical outputs into internal applications.

Senior Data Scientist – AI/ML at Stripe

August 1, 2021 - January 1, 2023

Developed and deployed ML models for customer segmentation, churn prediction, fraud detection, and demand forecasting; designed end-to-end ML workflows from data collection to model deployment; applied supervised and unsupervised learning; performed EDA and hypothesis testing; built NLP-based models for text analytics; deployed models via REST APIs; leveraged cloud platforms (AWS/GCP/Azure) and Spark for big data processing; created dashboards in Power BI/Tableau to communicate insights; automated model training and evaluation pipelines; collaborated with product and engineering teams to translate requirements into scalable analytics solutions; ensured governance and reproducibility.

Senior Data Scientist – AI/ML & Generative AI at Plaid

February 1, 2023 - Present

Designed and deployed end-to-end ML and Generative AI solutions to solve complex business problems and improve decision-making. Architected LLM-powered applications using Retrieval-Augmented Generation (RAG) to enable context-aware intelligent search and conversational AI experiences. Fine-tuned foundation models using LoRA/QLoRA to improve domain-specific accuracy while reducing inference costs. Built scalable ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. Developed transformer-based NLP models for classification, summarization, and semantic similarity; implemented vector databases (FAISS, Pinecone) for high-performance semantic retrieval. Deployed containerized AI workloads on Kubernetes and cloud platforms (AWS/Azure/GCP) and established MLOps practices (MLflow, Airflow) for experiments, versioning, and automated retraining. Designed real-time and batch inference pipelines with rigorous monitoring to detect data dri

Senior Data Scientist – AI/ML at BlackRock

August 1, 2021 - January 1, 2023

Developed and deployed ML models addressing customer segmentation, churn, fraud detection, and demand forecasting. Built end-to-end ML workflows from data collection to production deployment. Applied supervised and unsupervised techniques including regression, classification, clustering, and ensemble methods; conducted EDA to reveal patterns and inform business strategy. Engineered scalable data pipelines using Apache Spark and cloud platforms (GCP/AWS) to support real-time decision making. Built NLP-based models for text analytics and sentiment assessment; delivered dashboards with Power BI/Tableau for leadership visibility. Enabled REST API-based real-time and batch inference; collaborated with product managers and engineers to operationalize analytics in production while upholding governance and data quality.

Senior Data Scientist – AI/ML at Wayfair

February 1, 2019 - July 1, 2021

Built ML models to support customer analytics, sales forecasting, and risk modeling. Processed large-scale data with Hadoop (HDFS, Hive) and Spark; created statistical prototypes in Python and R. Performed EDA and feature engineering to improve model readiness; engineered predictive features for performance. Developed NLP solutions for text classification and sentiment analysis; created dashboards in Power BI/Tableau to communicate insights to stakeholders. Automated model training and evaluation workflows and deployed models via REST APIs for real-time and batch inference. Collaborated with data engineers and product teams to integrate analytics into business workflows and drive data-driven decision making.

Data Scientist – Machine Learning at Optum

August 1, 2016 - January 1, 2019

Assisted in developing ML models to analyze healthcare data and derive actionable insights. Performed data preprocessing, cleaning, and transformation; conducted EDA to identify trends and data quality issues. Implemented supervised learning algorithms and applied hypothesis testing to ensure statistical rigor and compliance. Built scalable data pipelines using Hadoop and Spark; partnered with cross-functional teams to deploy models, monitor performance, and ensure data governance. Supported feature engineering efforts and automated reporting to support operational decisions.