About
I care more about the question than the tool.
I’m a Data & BI Analyst at Accenture in Kolkata. Most of my work sits in the gap between a business question that is vaguely worded and a dataset that was never collected to answer it.

01
What I actually do all day.
The first version of a request is almost never the real one.
The first version of a request is almost never the real one. “Can you pull the numbers for Q3” usually means someone has a decision to make and a hypothesis they have not said out loud. I spend more time than most people expect on finding the decision behind the request, because the decision determines what precision is actually required.
Once the question is clear I try to answer it in the least clever way that works. Clever analysis is difficult to check, difficult to hand over, and difficult to defend six months later when someone asks why a number moved. Where a descriptive breakdown will settle the argument, I use a descriptive breakdown.
Where it will not, I state the uncertainty explicitly. Confidence intervals, effect sizes, and cell counts go in the output rather than in a footnote — not to hedge, but because a segment of eleven rows and a segment of eleven thousand should not look equally authoritative on a dashboard.
02
The thing that changed how I work.
I learned where data is allowed to travel the expensive way, and then built around it.
I write for the person who will act on the result, not for the person who will grade the method. That means leading with what changed and what it implies, then making the working available underneath for anyone who wants it. The analysis has to survive being read by someone in a hurry.
I ask about data boundaries during scoping rather than at review. Finding out where customer text is allowed to travel after the analysis is finished is an expensive way to learn it, which is part of why I built a text-clustering tool that runs entirely on-device.
And I try to make work reproducible by default. Tests, pinned inputs, and committed configuration are not engineering ceremony — they are what lets me still believe my own numbers after I have forgotten how I produced them.
03
What I’m after next.
Messy at the edges is the good kind.
Analysis, BI, and reporting work where rigor matters as much as clarity — ideally somewhere the analyst is close enough to the decision to see whether the work changed anything.
I’m most useful on problems that are messy at the edges: unstructured text mixed with structured records, reporting that has grown organically and needs rebuilding, or a recurring question nobody has yet turned into a system.
04
Verified credentials.
Every link goes to the issuer, not to a screenshot.
- Claude Certified Architect: FoundationsAnthropic ↗
- Google AI SpecializationGoogle / Coursera ↗
- Power BI Data Analyst AssociateMicrosoft / PL-300 ↗
- Associate Data PractitionerGoogle Cloud ↗
- Professional Data EngineerGoogle Cloud ↗
- Azure FundamentalsMicrosoft / AZ-900 ↗
- Security, Compliance, and Identity FundamentalsMicrosoft / SC-900 ↗
Education
2019 — 2022
B.Tech in Computer Science & Engineering
Techno International New Town
2016 — 2019
Diploma in Computer Science & Technology
Budge Budge Institute of Technology