HackerEarth is a community of over 4 million developers who participate in online hackathons and programming challenges in preparation for a career in the tech industry. Several prominent companies also use HackerEarth as a screening platform for recruiting prospective employees.
The team recently interviewed me from HackerEarth for their newly launched podcast series titled “Breaking 404”, which as they describe it, is a podcast for all engineering enthusiasts and professionals to learn from leaders in the engineering and technology industry.
Until earlier this year, I used to think that podcasts seem to be all the rage in the industry of late, but I realized their value only when I started listening to them during my regular runs. Data Skeptic, Econ Talk and This week in machine learning and artificial intelligence (TWIML), are part of my playlists in Spotify when I go running these days. There are a lot of amazing podcasts, blogs, and videos from people I admire a lot, telling their story of getting into data science, which I wish I had access to when I was starting my career. So when the HackerEarth team approached me to be interviewed for their podcast last month, I thought it might be an opportunity for me to share my journey into machine learning with their developer community. I know that it may at best be only footnotes in the annals of those whose careers were transformed by data science and machine learning.
In addition to reflecting on my early days as a software engineer in India and how it inspired me to pursue graduate school in computer science/ML in the US and thus accelerating my transition into data science and machine learning, I also talked about my current second innings at Salesforce after I returned as a boomerang, over five years since I left it as a software engineer. I also had a chance to touch upon the work we do in building Salesforce Einstein, on how we uphold trust while maintaining a healthy balance between doing exciting work on B2B machine learning while consistently delivering a high-quality product for our customers. We also discussed a question that’s confused a lot of young job seekers recently - what is the difference between a data scientist and an ML engineer?.