Heading the analytics and personalisation teams at BankBaazar, Varsha Mahadevan has been focusing extensively on building a data platform that is the core of the organisation’s organic growth strategy. She also delivers the technology stack for business intelligence reports. Before BankBazaar, Mahadevan was a Development Manager at Microsoft in Seattle leading a variety of different solutions ranging from Consumer products like Cortana, the Microsoft AI agent to Enterprise products such as Systems Center and Developer products like Net Platform. In this detailed interaction, she shares her views on being a woman in tech and leading her way all the way up to being a leader in the space.
Analytics India Magazine: Have you ever faced gender disparity in your career in analytics and data science? How did you fight the obstacles?
Varsha Mahadevan: I tend to carefully choose who I work for. So no, I have not faced any gender disparity within the organisation that I work for. However, I have indeed experienced certain biases when interacting with individuals outside of my organisation. When I would go in with a male colleague into a room, there would be inherent assumptions made about my seniority. To bust some of the assumptions early on, I learned to always introduce myself with my designation. This also aided in correcting certain behavioural traits that came along with these wrong assumptions. Another thing I learnt is to LOOK and DRESS your part. These observations are probably not specific to data science and apply widely in the industry.
AIM: Research suggests that girls are less likely to study STEM subjects. How can we inspire young girls to get involved with technology careers?
VM: To this, I’d say, know your strengths and follow your heart. I picked my area of study even before I knew what STEM stood for. I grew up in a suburb in Mumbai in a family with a deep-rooted value for education where I learnt to pursue engineering with all my heart. To be honest, I did not see any difference in passion among both men and women in my peer group. They were all high aspirers independent of gender and have all done well for themselves in life now. But I do acknowledge that the story is not quite the same all over. While I worked at Microsoft in Seattle, I volunteered for the DigiGirlz Program that worked towards promoting STEM in schools in the United States. I am sure that programs like these could help provide early clarity in career choices to girls in Indian high schools as well.
AIM: What would be your words of advice for fellow women professionals who are looking to switch or start a career in data science?
VM: My philosophy has always been to choose the problem space that excites you first. Technology is a means to an end. So, if data science excites you, first hone in on a problem space that is rife with opportunities for the use of data science. And then mould your skillsets to suit the position you seek.
AIM: Do you think there is an unconscious bias in recruiting women in technology? If so, what are the ways we can overcome it? Have you faced it in your analytics and data science career?
VM: Again, I have not experienced this first hand in the organisations that I have worked for. However, I have been in other conversations, where gender diversity is discussed as a cosmetic measure. There appears to be a lack of deeper understanding of these values even amongst very senior management of top tier organisations. In my opinion, running experiential programs that allow these highly influential individuals to embrace these values with conviction is key.
AIM: Is there a need to re-starter programs by leading MNCs to help women get back into the workforce after a break? How can these programs help in uplifting women in tech? Does your company have any such programs?
VM: I have a slightly different take on this. More than restarter programs, it is important to grow the social fabric that enables women to restart careers that they have been on a hiatus from. Invariably their break tends to rivet around childcare (immediately following childbirth or perhaps even a little later). If organisations had a better policy around parental leave for fathers, this would organically address the need for women to take a break in the first place.
AIM: What are the various other steps that companies can take to increase the number of women in the technological field or for that matter even retain them?
VM: Conscious hiring goals, congenial and supportive work environment, appropriate parental leave policies around childbirth (both for mother and father), flexible work arrangements (for both genders) that support raising a family alongside a strong career.
AIM: Do women in senior management roles have to tackle the ‘prove it again’ bias?
VM: Perhaps it is true, but it is hard to tell. Personally, I am a firm believer that one should know what they bring to the table and must back oneself through thick and thin to stay that course.
AIM: What are the measures that can be put in place to help women rise to senior management roles in data science and analytics field?
VM: Early career guidance and sponsorship can go a long way in helping talented individuals aim higher and get there.
AIM: Is there a need for mentorship for women to help them accelerate their careers?
VM: Yes, there are certain behavioural traits in women that are dominant compared to the opposite gender, such as the tendency to be too self-critical. Some degree of mentorship can be handy to help balance out these traits and not over-index on them. I have seen this open up horizons for several talented individuals.
AIM: What would be your tips for maintaining work-life balance together?
VM: Prioritisation is the key. It took me a while to come to terms with the fact that I cannot do it all. However, I contribute to the most ‘high value’ activities in my day. I apply this unequivocally to all facets of my life, personal and work. I am very lucid in my communication about these ‘high value’ activities and set all the right expectations. This has certainly helped me keep things in check and make conscious choices.
