I build things that run without me.

I work on credit risk and portfolio analytics at Axis Bank, where most of my job is turning work that was done by hand into work that runs on a schedule. Outside it I build small, complete systems and put them online — a price index, a recommender, a reader, a few risk tools — and keep them running.

28
things live on the internet right now
18.8M
grocery prices collected and counted
55
projects since 2021, all of them catalogued
41
certificates, every one verifiable
Latest · October 2026

What do you swap when the shelf is short?

Seven staples, one Mumbai DMart, sixty-three mornings of Bhav's recorded shelf prices. Three of the seven were missing on 4 October — recorded out of stock, with a time — and the list still had to be bought. The page is that morning: a shelf you can re-plan by hand, a budget on a brass wire, and a receipt for every substitution it makes. Nothing is fetched and nothing is guessed: a missing price stays missing.

Filling the gaps is not the same as affording the basket. Three substitutions made all seven present for ₹1,020 — still ₹20 over. The fourth bought no gap at all; it swapped the tea, and took the basket to ₹904.

₹904
four changes make the basket complete on 4 October, ₹96 under a ₹1,000 budget
₹894
the floor, at six changes — the last two swaps buy ₹10 between them
1,825
recorded SKU-days; 161 recorded out of stock, two rows never observed
Selected work

Built end to end, and still running.

Each of these collects its own data, decides something, and keeps doing it on a schedule without me. The interesting part is usually the failure that taught it to.

Bhav on screen
Price index

Bhav

An inflation index built from what shops actually charge.

LiveRead
SenpaiScope on screen
Recommender

SenpaiScope

Anime recommendations that tell you why.

LiveRead
Foglight on screen
Reader

Foglight

A reader for the books that are hard on purpose.

LiveRead
Emotion Recognition on screen
Machine learning

Emotion Recognition

A facial-expression CNN that runs in your browser.

LiveRead
H
Autonomous systems

Hermes

A personal AI system that acts on its own schedule.

PrivateRead
Day job

Risk analytics at Axis Bank.

I sit in the Risk Analytics team, working on credit risk and portfolio analytics. The through-line of the last two years has been automation: moving the team off hand-run SAS MIS onto Power BI that refreshes itself, migrating 100+ SAS tasks to PySpark on the Big Data Lake, and building the monitoring layer that sits on top of the result.

Risk & domain
Credit risk · Portfolio analytics · Enterprise risk management · Emerging & change risk · CECL · Corporate forecasting
Data
Python · PySpark · SQL — Hive, Spark, Oracle, Impala · Credit risk modelling · PostgreSQL
Platforms & BI
Power BI — DAX, Power Query, semantic models · Tableau · Informatica Cloud · Azure Data Lake · Dataverse
Automation & GenAI
Power Automate · Copilot Studio · LLM applications · Bitbucket · Jenkins
Building things
React · TypeScript · PyTorch · ONNX · Docker · Caddy · systemd
Certifications

41 certificates, all of them checkable.

Three Google professional certificates, courses from IBM, UC Davis and Coursera, and a McKinsey.org programme; around them, HackerRank's skill tests, two Google Cloud certificates from Google Developer Student Clubs and a face-recognition build from HCL GUVI. Each one comes with the issuer's own certificate and a link to check it.

97.3%
mean grade across 28 graded courses
10
courses finished at 100%
471
hours of coursework
3
Google professional certificates
Google Project Management — certificate issued by Google to Anamaya Sharma, Dec 2022Google Data Analytics — certificate issued by Google to Anamaya Sharma, Dec 2022Google IT Automation with Python — certificate issued by Google to Anamaya Sharma, Aug 2022
Research

Measuring the measurement.

Built on SenpaiScope. The paper separates three things that offline recommender evaluation usually conflates — the answer key, the metric, and the scorer — and shows each one independently changing which system wins.

The code, the data and every table are public, and section 8.4 re-runs the whole comparison against a different answer key and withdraws one of the paper's own earlier conclusions — which is the part I would defend.

Looking for the next problem.

Risk, data platforms, or anything where the measurement is the hard part. I read every email.