Five years of finding signal.
Now building what follows.
A career that moved from electrical engineering through deep learning research and data science — and is now focused on product. The through-line has always been the same: find what the data is actually saying, then turn it into something useful.
EEE 2013–2017
Analyst 2017–2019
Data Science 2021–2022
Data Scientist 2022–2024
AI & Data 2024–present
Started in electrical engineering — where the work is about understanding how systems behave. Moved into data science because the problems were more interesting and the tools were better suited to answering them. Published in IEEE on deep learning classification of pseudogenes in the human genome. That research required the same discipline every data problem does: define what correct looks like before you touch the model.
From there: data analyst at a power sector consultancy in Vijayawada, data scientist at Profitops.AI (remote, US-based) working with LLMs, causal inference, and MLOps pipelines. Then product manager at The Siasat Daily — where the work shifted from building models to building the infrastructure and decision-making context around them.
The move to product wasn't a pivot. It was a recognition that the most interesting problems sit at the intersection of data, people, and systems — and that product management is where those problems get solved. The domain changed. The instinct didn't.
Reads across neuroscience, philosophy of mind, and astrophysics — not as a professional interest, but because patterns from one field tend to show up in completely different ones.
- Built and deployed Tableau and Power BI dashboards to monitor operational metrics across programs — reduced manual reporting effort by 70%
- Designed end-to-end ETL pipelines from PostgreSQL, Excel, and API feeds using Python and SQL
- Collaborated with stakeholders to turn business requirements into automated reporting infrastructure
- Data in the room for editorial decisions that had previously been made entirely by intuition
- Implemented and fine-tuned LLMs for multiple production use cases
- Applied causal inference (DoWhy) in ML projects to identify cause-effect relationships beyond correlation
- Built RESTful APIs with FastAPI to automate data workflows across internal systems
- MLOps: model deployment and lifecycle management with Docker and CI/CD pipelines
- Conducted data analysis on power sector trends; developed strategic insights for regulatory and policy decisions
- Built actionable reports and dashboards in SQL, Power BI, and Tableau
- Evaluated business models and assessed market risks to support compliance work
- Designed the UI for an online trading platform; user engagement up 30%
- Developed and executed test plans in Agile with 95% UAT success rate
- Bridged technical implementation and business requirements — first real exposure to the gap between what's built and what's needed
End-to-end analytical projects — from raw data to product decision. Each study applies causal inference, ML, and product thinking together, with every step explained.
what you're building.
Open to product roles, interesting problems, and conversations that start with "have you considered..."