Analytics India Magazine

To understand what it takes to become a data science professional, we are reaching out to experienced data scientists every week from the industry. For this week’s “My Journey In Data Science” we talked to Mathangi Sri, Head of Data Science at PhonePe. Mathangi has a strong track record of building world-class data science solutions and teams. Before PhonePe, she has worked with some of the top organisations and startups. She has also published more than 20 global patents in data sciences with 10 global patent grants. How Data Science Happened Mathangi’s journey in data science is undoubtedly one of the most interesting ones. As a child, she was very interested in poetry and literature, and she even dreamt of becoming an author. However, with time, she also developed a flair for mathematics and statistics. When asked about how this culminated into engineering, Mathangi said tartly, “Peer pressure”. By the end of her engineering degree, Mathangi started to feel a strong passion for management, and she ended up doing an MBA from NIT Trichy. When asked about the driving force that got her into data science, she said that once GE (currently Genpact) had come to their campus, and they talked about how they want to choose the locations for windmills based on data. This caught her attention and what she started wondering how they could do that with the power of data. And this is when she fell in love with analytics 15 years ago. The First Job Story Mathangi was one of the early entrants when the analytics market was picking up in India. She started her analytics career, that is, her first job, with GE (currently Genpact). She added that she didn’t have to struggle too much to land that job. However, she had to wait for the right opportunity. During her initial days of career at Genpact, she used to create lots of dashboards and reports. She used to use SAS for crunching millions of data. “This experience anchored me to be very disciplined about numbers. We used to analyse the drop in risk rates in a few basis points. This gave me the training to look for minor variations in data and get very rigorous about it,” said Mathangi. When asked about her first interview experience, Mathangi said she had a telephonic interview — the questions were around statistics fundamentals and on logic. Further, they also assessed her communication skills, which is also one of the vital aspects of data science. She also added that when it was about the learning phase she has learnt fundamentally on the job. And apart from that, videos, data science blogs, peers and team members have been the source of knowledge for her. She also believes that when it comes to training, practice is one of the not-to-ignore things in data science. “Immense amount of practice in applying techniques to real-world problems has been my best training,” Mathangi added. This Is How Mathangi Handles Setbacks Setbacks are not always in the form of failures but sometimes it’s also in the form of feeling — mostly self-doubt — that you get at a certain point of time. And data science professional experience this feeling at least once in their career. All data scientists suffer from the Imposter Syndrome at least once in their careers. Imposter syndrome is one such feeling that is common with data science professionals. And Mathangi often suffers from this. “I do feel I am an outsider faking it,” said Mathangi. She also believes that this syndrome only goes up with experience and never comes down. However, she often tries to be as much hands-on as possible, which she believes is one way of combating the syndrome. “I am not sure if I have figured out how to deal with it except sometimes ignoring it and just get going with the job at hand,” said Mathangi. Another setback is interview rejections. When we asked Mathangi about her take on this she said, “Rejection does feel hard.” And it feels really hard to handle when someone puts in tremendous efforts. But she also believes that without rejection there are no milestones that can be achieved. “Sometimes rejections are also a blessing in disguise — when things don’t work out they are generally for the right reasons.” The Best And Worst Experience While Mathangi talked about her data science journey, she also shared her best and worst experience in the domain. She said that one of the best experience was when they built text mining platform during her days at [24]7.ai. The platform was built for those with a text mining need and this platform would help them self serve. Further, she is also very excited about some of the personalisation work that she and the team is doing currently at PhonePe. Talking about the worst experience, she said, “Not every idea, project or a predictive solution gets implemented in data science. So there are many casualties from that perspective. I would not say this is the worst experience or something but I do think this is something that needs to change.” A Piece Of Advice For Aspiring Data Scientists The PhonePe data scientist has also shared some of the advice that she would like to give the aspiring data scientists. She said that one of the best ways to work is by putting data and business at the forefront. She also believes that patience is understated. But this is a virtue that will take one far. Moreover, she also added that the way data science gets adopted across organisations, there is a lot of stakeholder understanding that needs to be built. Unfortunately in academia, stakeholder management part of data science is not addressed. There are a lot of sensitivities to take a predictive solution to live in large organisations. The data scientists have to understand the business nuances to succeed.