Madhav Mishra

I’m a Senior Data Scientist with over 7+ years of experience building data and AI solutions that solve real business problems at scale. Most of my work has been in telecom and enterprise environments, where I’ve helped teams turn complex, messy data into insights and systems that actually drive outcomes.

At Telstra, I’ve worked end-to-end on initiatives ranging from OSS billing optimization and intelligent automation to customer lifetime value (CLTV) modeling and churn reduction. This has involved designing scalable SQL and Spark based data pipelines, building machine learning models for prediction and classification, and more recently, implementing LLM powered solutions that can explain complex billing logic in a way customers and stakeholders can clearly understand and trust.

My background combines hands-on machine learning and big-data processing with cloud-native architectures across Azure and AWS. I care a lot about how solutions are built, deployed, and scaled in the real world not just how models perform in isolation.

What really defines my work is the mix of deep technical execution and a strong product mindset. I enjoy taking ambiguous business problems, breaking them down, and turning them into production-ready, reliable solutions. Over the years, I’ve worked extensively with Azure (ADF, Databricks, ADLS), PySpark, Spark SQL, NLP, DL, ML monitoring, drift detection, and human-in-the-loop AI systems to ensure systems remain accurate, explainable, and trustworthy at scale.

Before moving fully into data science, I worked as a Product Manager in Analytics. That experience helps me communicate comfortably with engineers, business teams, and leadership, and keeps me focused on impact rather than just implementation.

I enjoy working on problems related to:
• Machine Learning & Predictive Modeling (ML, DL)
• GenAI / LLM Applications
• Customer Analytics & Revenue Intelligence
• Data Engineering & Cloud Platforms
• Automation & Explainable AI

I’m currently open to Senior Data Scientist or Data Science Lead roles where data, AI, and business strategy come together to create meaningful, measurable impact.

Agentic RAG for Shrimad Bhagwat Geeta: Builds an intelligent RAG framework that discerns when to directly answer or strategically search Geeta texts. It moves 'From Retrieval to Reasoning,' providing nuanced, context aware insights from spiritual teachings by intelligently deciding what, where, and how to retrieve information.

https://github.com/DeltaOptimist/From-Retrieval-to-Reasoning-Building-an-Agentic-RAG-Framework-for-Shrimad-Bhagwat-Geeta