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Everybody strives to accomplish big things when they plunge head-first into the industry. But some hustlers keep it low-key, quietly leading the way in the tech space.
Analytics India Magazine interacted with one such woman in tech – Pragya Mishra, business analyst (data science and analytics), VMware – giving us insights into an ideal inclusive workplace.
Right from handling business operations to now mentoring students traversing the same path, Pragya shares her learnings on how to make progress as a data scientist. A core member of Women in Analytics at VMware, Pragya holds a degree in engineering.
AIM: Narrate a typical day in the life of a business analyst.
Pragya: Organisations use tonnes of data. Business functions like treasury, legal, and internal audit are constantly trying to break down their massive business problems into small chunks and parts. Being part of the chief digital transformation office, we lead initiatives, working in a sprint fashion. A meeting is held with the senior management every 15 days, to plan out activities that pivot on delivering impact. We cater to multiple business functions with a prime focus on chief data oddities. And there are always checklists to take care of smaller tasks and goals.
AIM: What are the roles and responsibilities that come with being a part of the chief data office at VMware?
Pragya: As a business analyst, my responsibilities include connecting IT and the business teams, understanding business problems and their impact, analysing various processes involved, and gathering detailed requirements. I also indulge in data and business solutions, in-depth data preprocessing and analysis to deliver data-driven recommendations using analytics and advanced machine learning. Additionally, I also communicate the insights and findings with the higher management and deliver effective products and business solutions.
AIM: Please highlight some of your contributions to the company and the data science community, in particular.
Pragya: Success to me is sharing my learnings. One can never learn everything at once – it’s a never-ending process. That’s why I chose to contribute to the data science community by starting the ‘Wabi Sabi Initiative’ – a not-for-profit mentorship program inspired by the Japanese philosophy of embracing imperfections. It aims to assist new learners in the field of analytics and data, enabling them to develop the required skill sets.
I also work as a subject matter expert and video content creator at upGrad, one of the leading edtech startups in India, by creating written content on data science and business analytics. When you’re mentoring or creating content, you constantly learn along with them. It’s a two-way process that doesn’t stop.
Other than that, I am an active participant in Kaggle competitions, which helps me update my knowledge in the data science industry.
AIM: Could you talk in detail about your educational qualifications and previous work experience?
Pragya: I have a degree in electronics and communication. I was always trying to solve and narrate things, call it data storytelling. So when I came across a big data analytics company named Mu Sigma, I was completely fascinated by how they solved business problems for Fortune 500 companies. I got into the role of a decision scientist with them.
AIM: How do you approach a data science problem and ensure the work goes smoothly as planned?
Pragya: One of the major hurdles is the chaotic problems that data scientists are trying to solve. It’s never straightforward. One should be aware of the problem by constantly learning in the domain from their peers and leaders. To solve a problem smoothly, one should have the right data sources to clean and pre-process and mitigate any risks. Certain questions may arise, like, do we have the right data? And when we do, we must clean it enough, as one is likely to come across tonnes of unclean data daily.
AIM: How do you plan to grow in the data science ecosystem in the next five years?
Pragya: I envision myself evolving with the data science and product innovation community, collaborating with lots of experienced data folks, budding data scientists and analysts through online/offline associations. I would continue being part of initiatives with massive social and economic impact. Definitely great times ahead!
AIM: As a part of Women in Analytics at VMware, tell us about the company’s goals for a diverse and inclusive environment?
Pragya: By inclusion, we mean bringing together the power of different people from different backgrounds to the company. We become more aware of the challenges they faced, bringing different stories to the table. Being a core team member of the Women in Analytics at VMware, I wish to see an equitable environment that breaks all barriers of gender inequality.
AIM: How can organisations address gender inequality at the workplace? What measures can be taken to tackle such challenges?
The only way to battle unconscious biases at work is to hire more diverse individuals. When we discuss hiring different genders and races, we are united in understanding unrecognised patterns at work. We talk to them, acknowledging the challenges they face. This is something that I have learnt from my leaders. Such conversations will help organisations to tackle these problems.
1) Favourite ML/AI algorithm
The machine learning models change so fast and so frequently in the industry, I won’t have a favourite model. But depending on the classification, I would say ensemble models are the ones I always go for. It’s because of the massive loads of data that they can handle.
2) Favourite book on data analytics
My recommendation to anyone who’s starting off is ‘Statistics for Dummies’ by Deborah J. Rumsey and ‘Business Analytics’ by Dinesh Kumar. However, ‘Ace the Data Science Interview’ by Kevin Huo and Nick Singh tops my list!
3) Favourite podcast in AI and problem solving
My favourite is Lex Fridman on Spotify – he is an AI researcher at MIT and his podcast mostly ties AI to problem solving, space travel, neuroscience, human-robot interaction, etc. He’s one of the best out there.
4) What would you be, if not a data scientist?
For the love of storytelling, I would be a consultant mostly who is, again, telling another data story in some other part of the world (or a scriptwriter!).
5) Your advice to women who want to go this path?
Constantly upgrade your technical skills. Period. Whichever gender you belong to, I think one thing that will always keep you in the game is if you constantly ace your problem-solving skills. Second, always speak up and take a stand for yourself, because if you don’t fight for yourself, no one else will.
