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Ramakrishna Reddy Vennam

@ramakrishnareddyvenn

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Data Scientist at CHARLES SCHWAB building RAG solutions for financial insights that cut analysis time by 40%.

United States

Message

At CHARLES SCHWAB, I developed RAG solutions using LLaMA, Azure OpenAI, and Azure AI Search to streamline financial query resolution and cut analysis time by 40%. I also built credit-risk classification models across 3M+ records and real-time Power BI dashboards for executive metrics.

At OPTUM, I built PySpark pipelines in Azure Databricks and predictive models using clinical and claims data. I developed NLP models to extract clinical entities while masking PHI/PII, and established MLflow workflows for model tracking and deployment.

Experience

Work history, roles, and key accomplishments

Current

Data Scientist

Current

Charles Schwab

Jun 2025 - Present (1 year 4 months)

Leading data science and GenAI initiatives for credit-risk decisioning and document analysis. Developed predictive models, RAG architectures, and REST APIs, improving detection accuracy by 25% and cutting analysis time by 40%.

Python SQL Power BI RAG LLaMA Azure OpenAI Azure AI Search AutoGen NLP Docker Flask SQLAlchemy AWS Azure KNN Gradient Boosting Hypothesis Testing regression analysis

Healthcare Data Scientist

Optum

May 2020 - Jul 2023 (3 years 2 months)

Spearheaded predictive analytics on multi-terabyte claims datasets to improve CMS Star Ratings and clinical outcomes. Built PySpark pipelines, deployed HIPAA-compliant NLP models, and established MLOps frameworks.