Ashka Katira

I started my career back in India in 2013 as a software engineer in a consulting firm, working with many clients. During one of my projects, I had the opportunity to work with data scientists and data people, and that experience really sparked my interest. I did a mini-project with them, which led me to pursue my master's in data science and make the career switch. Now with more than 10 years of experience, my main areas of expertise are in traditional ML, where I focus on prediction, finding insights from data, predicting retention and churn, and understanding company health overall using ML. I also work with causal inference, finding the root cause of why something happened and what is causing that. Recently, I've been spanning into a new area of how we can do ML with AI in AI. My typical day varies based on where we are in the project lifecycle. I start with understanding the business context and where data science can actually help the business. A lot of my role involves stakeholder management throughout, because understanding company health is both art and science. I do the more scientific part, but I also help business leaders understand where we can utilize those results. So my typical day has both parts: working with C-level and business stakeholders, and understanding the technical side and where we can utilize data science methodology to get the right business decisions.