I'm a Data Science Manager at a leading US insurance firm, where I build production LLM pipelines, realtime fraud detection systems, and causal inference engines that drive millions in risk mitigation. I lead technical teams on building AI powered decision intelligence systems that actually work in production, not just in notebooks.
My technical identity bridges rigorous statistical foundations with modern LLM engineering. I believe in AI powered decision intelligence, the intersection of causal reasoning, Bayesian thinking, and production-grade orchestration
. Epistemic rigor matters: every model claim should come with calibrated confidence.
I'm actively building: open source Bayesian network research libraries, public writing on production ML systems, and leading teams toward a vision of trustworthy, interpretable AI at enterprise scale. Moving toward Director/VP of AI & Decision Intelligence.
12+ years in data science and AI engineering
How I Think
Three core principles that guide my approach to AI and ML systems.
Epistemic Rigor First
Every model claim should have calibrated confidence. Uncertainty quantification isn't a nice-to-have—it's foundational to trustworthy AI.
Systems Over Models
ML value lives in end-to-end pipelines, not isolated notebooks. Architecture, orchestration, and monitoring matter as much as model performance.
Lead With Curiosity
Stay close to first principles even at leadership level. The best technical leaders keep their hands dirty with actual systems.
My Journey
From statistical foundations to production decision intelligence.
2008
Started School in Computer Science
Fell in love with algorithms, data structures, and the power of code to solve complex problems. Built a strong foundation in programming and software engineering.
2012
First Job in working with Large Datasets
Built ETL pipelines and data warehouses for a major financial company, learning the importance of data quality, scalability, and real-world constraints in production systems.
2015
Back to School for Master in CS
Learned rigorous statistical inference, algorithms, and machine learning theory at Indian Statistical Institute. This mathematical grounding shaped my approach to production systems—when you understand the theory deeply, you make better engineering trade-offs.
2017
Large Scale ML in Production - Cybersecurity
Built single-handedly production ML pipelines for real-time cyber threat detection for a startup. This was my first deep dive into the challenges of deploying ML at scale—data drift, latency constraints, and the importance of monitoring and feedback loops.
2020
ML in HR Tech - Leading AI for Talent Acquisition
Led the development of AI-powered talent acquisition systems, leveraging machine learning to optimize hiring processes and improve candidate matching.
2024 - Now
AI in Insurance - Building Decision Intelligence Systems
Building production LLM pipelines, real-time fraud detection systems, and causal inference engines that drive millions in risk mitigation. Leading technical teams on building AI-powered decision intelligence systems that actually work in production—not just in notebooks.
Tools & Stack
Technologies I work with daily to build production AI systems.
Education
Master's Degree, Computer Science
Indian Statistical Institute (ISI), Kolkata
Foundational training in machine learning, statistical inference, and algorithms. This mathematical grounding shaped my approach to production systems—when you understand the theory deeply, you make better engineering trade-offs.