"Sometimes the beauty of being able to use all of the science and knowledge is to change the question into something that's more reasonable to answer with the enormous data... Don't paint everything with one board brush and say, “This is the pangalactic answer for everything we're going to do with data."
Akshay heads up Data Analytics and Data Management at Fundnel. He works closely with the Product team to leverage artificial intelligence and machine learning capabilities to enhance the platform. He is also responsible for streamlining the data collection and data management strategy for Business Intelligence. Prior to joining Fundnel, he co-founded a cyber security startup which was named Top 30 Startups by Forbes India in 2016. Akshay has spent over 5 years developing Big Data and Artificial Intelligence products for the Fintech and Healthcare sectors, in Singapore and India. He also brings experience from Singapore Diamond Investment Exchange where he involved in quantitative and statistical research. He graduated with a Masters in Knowledge Engineering (Data Science and Analytics) from National University of Singapore.
Call it an accident or just luck, I stumbled upon a role in business intelligence and data management right after getting a degree in computer science. I immediately found myself deeply connected with the work and started enjoying myself. My interest has always been sparked by anything data-related, so I was sure about progressing in my career in analytics. I've never looked back and continue to push myself to improve my skills. Today, I feel happy with what I've achieved so far and am satisfied with my work.
What I've Learnt
Machine learning is very small fraction of data science. More than half of my time is spent on building the infrastructure and data pre-processing pipelines. It’s not the sexiest part of data science but when you get the infrastructure and pipelines right, everything falls into place – the analysis is faster, more conclusive and much more reliable. A lot of data scientists miss the importance of an infrastructure, which would seriously constrain the speed, scope, and quality of work. Every now and then, my work calls for statistics/machine learning but it's usually the last step in a long data pipeline. Infrastructure, data processing pipelines, data analysis and machine learning are like the mechanical gears of an engine; if one fails, the entire engine is useless.