Women Face Behavioural Biases, The Best Way To Overcome Is To Stay Assertive: Toshi Prakash, Locus.sh
Toshi Prakash, who is the VP Product at Locus, has been working as a product manager for the last five years and had worked as the software development lead for eight years before that. With extensive experience in building sustainable and scalable product design, she has built both large-scale enterprise-level products and small fun apps from scratch, handling every aspect of the life-cycle from idea to release, growth and demise. In her current role, she heads the product at Locus that use AI, ML to optimise the supply-chain logistics of enterprises. She has earlier worked for Freshmenu and Nestaway to help them build the products from scratch and scale it. In this interview, she shared her bit of challenges that she faced being a woman in tech background and how she overcame it. Analytics India Magazine: Have you ever faced gender disparity in your career in analytics and data science? How did you fight the obstacles? TP: Like every woman, I have faced the typical behavioural biases. I have been cut-off in meetings, have been discredited from the ideas that I brought to the table,  and have been told that I am rude and intense; even when aggression from the men in the same room goes completely unnoticed. I have been over-looked for promotions despite being quantifiably more deserving because my ‘network’ wasn’t significant. To counter-balance the lunch and smoke talks that my peers had with superiors, I had to make extra and very formal efforts to showcase the work I do. My approach is to be assertive yet calm in such situations. It takes a little extra effort in figuring out alternative ways than the usual ones that are presented to us, but it’s not impossible. Good work and talent always shine through. AIM: Research suggests that girls are less likely to study STEM subjects. How can we inspire young girls to get involved with technology careers? TP: It seems very paradoxical. We see that during early school years girls performing at par and in equal numbers with boys. But somehow, when it comes to choosing careers, they choose non-technical courses. I believe that this has a lot to do with the guidance they get when making these choices and what role-models are in front of them. If they can meet, talk and see women in STEM professions on equal footing as men, any naïve bias developing in their minds can be curbed. In particular, children learn more from what they see than what they are told. In schools and in media, we should bring as many women on the dais as men, and not just once or twice a year for a particular event. Subtle biases of cutting a lady off when talking should be controlled in any discussion. Even in family gatherings, they should be equally encouraged to participate in deeper discussions on career, politics, sports; and not just household stuff. AIM: What would be your advice for fellow women professionals who are looking to switch or start a career in data science? TP: Frankly, my advice here would be the same as that I would give to any person: If you like it and look forward to the time working on it, go for it. Passion should be the driving force and challenges just make us stronger. As a field, data science is mathematical yet exploratory and thus ideally suited for women who love finding answers. 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? TP: Over the past ten years, this has become less prominent, but the bias persists. Even today, a female candidate would probably not be offered certain roles, if she has plans to start a family soon. Equal pay for women is still a part of very few organisations, and the perception remains that women would not be working as many hours as men. Most of the times, the data on hours of work, refuses to account for the informal breaks taken during the day. Let us be aware of our biases and try to find ways to judge based on data before forming an opinion objectively. If the bias is based on hard work put in, we should build in measures to judge everyone’s productivity in a granular manner and make that the basis of any judgment call. Forthcoming maternity leaves should not be a reason to reject current candidature as one never knows what happens in anyone’s future and what type of leaves we may need. AIM: Is there a need to re-start 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? TP: Yes. I think there is just a little bit of extra encouragement needed to get back into the workforce after a break. At a time, when one is battling with constant doubts on whether they are making the right choices for themselves and their families, even the smallest of encouragements go a long way. Making it easier for their resumes to be considered after a career break, is a great start. Having training sessions to bring them up to speed to the latest developments in their projects, their field of work and the market trends make them confident of their capabilities again. Locus provides six months of paid leave and six months of ‘work from home’ in the case of maternity leave. Apart from that, in case of any medical or personal emergencies Locus always supports its employees in surviving through the period and smoothing out the comeback process after a break. 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? TP: The most significant change can be brought out by building an inclusive work environment. An environment which treats everyone as a human and not a resource. The parts of life that we leave to work in an office might affect our productivity. With women, this becomes apparent with family but for the same can be the case with men dealing tending to the elderly. Providing flexible office hours when needed and encouraging work from home when people are going through difficult times. Having adequate maternity leave policies and not considering them as an open position for new resource consideration, gives a woman the security she needs. Having a day-care facility also helps. Most of all the attitude of the organisation matters, if the approach is supportive and non-judgemental people like to continue to stay. AIM: Upskilling is one of the foremost requirements to sustain in the tech-driven industry such as data science. Are there enough opportunities within corporates for women to upskill? How could it be incorporated? TP: I think for upskilling in Data Science and technology, the resources are primarily available online via courses and research papers. The courses come with test and scores which have credibility in the market as well. I believe everyone should pursue these courses to enhance their skillsets and corporates should provide any financial and time-related support for those. Another skill set essential in Data Science is understanding the applications of the latest research improvements and the current market trends for the same.  The same can be enriched by attending conferences, meetups and reading online. Corporates should encourage participation of all members in such events. AIM: Do women in senior management roles have to tackle the ‘prove it again’ bias? TP: Yes. A lot. Every time a lady is promoted to senior management positions, she stands out. Questions on ‘why her’ do the rounds even when there are a lot more men getting promoted. She is supposed to prove again that she is as good, if not better than others, for the role. She may be sticking to regular office hours and not be available in the after-work networking events. And thus the questions on her soft skills, her ability to lead and to build her team start coming up. Objectively thinking these questions don’t stand a definitive ground, but a woman has to deal with them and face them again and again. 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? TP: Having more women in the leadership position who can understand the current biases, comes first. They should be encouraged to bring the current problems afflicting a team to the table and not be grilled on giving data-points as those are difficult to present retrospectively. We should build a support group where women can freely talk to other women, share their professional problems and advise each other on how to tackle them. AIM: Is there a need for mentorship for women to help them accelerate their careers? TP: Given that generally there are less than 10% of women in the leadership of any organization, I think mentors are definitely needed. Mentorship programs with 2, 3 level-up seniors (both men and women), can help women showcase her work, figure out the areas of growth and go up the career path. It can also help remove any bias that creeps in because of men being in each other’s friend circle. AIM: What would be your tips for maintaining work-life balance together? TP: A detailed and regular schedule helps a lot in finding that balance, so my suggestion would be to be assertive and stick to it as much as possible. But most of all never feel guilty of being human.
Toshi Prakash, who is the VP Product at Locus, has been working as a product manager for the last five years and had worked as the software development lead for eight years before that. With extensive experience in building sustainable and scalable product design, she has built both large-scale enterprise-level products and small fun apps […]
Having worked with companies such as Nestaway, she currently heads AI, ML products at Locus that optimise the supply-chain logistics of enterprises.
["Interviews and Discussions"]
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["AI", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go", "Scala", "GAN"]
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.