How I got here
I studied electronics and communication engineering at Vishwakarma Government Engineering College, under Gujarat Technological University, graduating in 2015. My first job was as a yield engineer at Maxim Integrated (Now Analog Devices), where the product was chips and the work was data: large volumes of manufacturing measurements, and which of them meant a tool was going to fail.
Then came data work closer to money. Alongside a master’s in Data Science at Monash, on a merit scholarship, I analysed insurance risk at PD Insurance, worked as a research assistant at Monash University’s SoDa Labs, and built a forecasting proof of concept for Dusk Mobile. After it, as a self-employed data scientist, I forecast logistics planning for Trimble and built credit scores and fraud models for Cash Direct, a lender.
In 2021 I joined Fundo Loans as lead data scientist. In a small team, we took an Australian lender from $1M MRR to a 3× multiple at acquisition, with 1.5× the loan book: I built its data architecture, automated decisioning and bank-statement analysis from scratch, and in-house automation that saved over $265K, approved by PwC.
On the side I kept building. A stock price analyser that read the sentiment of news about a company. A credit score modelled on chess ratings. A small Python package, basicanalysis (opens in a new tab).
Mundane Read started with a school report. In my words on its About page: “Back in school, my teacher docked marks on my report writing. When I asked her to review it, she explained that we should not use removable adjectives, overly descriptive words, or include personal opinions.” In 2019 the stock analyser pointed sentiment analysis at share prices; Mundane Read measures the writing itself.
On the side, I’m building Creditcrest Technologies, lending software, and Mundane Read, a news reader. Both are built on the same rule: a number comes with the working that produced it.
