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‘Data has a better idea.’
It’s a common saying among data scientists and analysts across organizations, that reinforces that when used properly, data can be converted into meaningful insights. But the question everyone misses is - where do we gather this data from?As a generation that has just begun to experiment with data, machines, and computation, it’s easy to forget about the common source of our information.
This is one of the most important lessons I’ve learnt at FinBox. The way we extract data from the most traditional, yet the most unexplored of sources is what makes my work here particularly enjoyable.I had the opportunity to work at another FinTech company prior to FinBox. But over my journey here, I’ve realized that the way FinBox operates is different from how most FinTechs in India work. We believe in a tech-first culture and leverage it to provide credit to a billion Indians.
What I do
I work at FinBox as a Data Scientist - but I wear different hats at different times. My job involves making decisions, deploying code by converting models from MegaBytes to KiloBytes (yes, we did that!), analyzing TeraBytes of data to identify patterns inherent to the Indian subcontinent.
What I particularly love is the independence I have to experiment with different solutions to finish at the optimal position. Finding solutions to new problems is always an incentive, and everyone is just a call away (WFH, sad).
I started off by creating regexs to gather information from pools of data, and then created different underwriting models for varied segments and deployed them into production. I learnt a lot here while converting a 30MB pickle to a 50KB file! I also worked on underwriting rules for lending purposes and leveraged AWS SageMaker for our data science stack.
It would be safe to say that I learnt a great deal about lending, data science and information capture during these 2 months at FinBox. It’s been a stupendous experience characterized by independence, ownership, and upskilling.
The ride has just begun!