Women In Analytics: In Conversation with a Data Science Leader
This 8th of March, and as we do all year round, we’re celebrating Women in Analytics. It’s common knowledge that the STEM (science, technology, engineering, medicine) fields are dominated by men, but that is beginning to change, and these women are leading the way. We spoke to Mousumi Ghosh, the Head of Fraud Analytics at Square (Caviar), about her journey in analytics so far, challenges she’s had to overcome along the way, and what aspiring analysts can do to give themselves the best possible chance of having a successful career.
Hi Mousumi, thank you for making the time to speak with us. To begin with, what was your educational background, and how did you end up entering the analytics space?
I did my masters in Systems Engineering from Virginia Tech University and also interned at a Management Consulting firm – that’s where I began my analytics career. I’ve always been good at mathematics, and I also love solving puzzles. So to me, analytics is a problem solving exercise which I thoroughly enjoy.
How has the analytics space changed from when you started out? What are some of the most significant improvements that you’ve observed?
A lot more data is available now, though a fair amount of it is unstructured. Every organization is constantly trying to leverage big data and analytics to benefit their business practices. Analytics is used in every area, and the sophistication of analytics has also evolved significantly over time. Companies are also increasingly using robotic automation, artificial intelligence and machine learning to get better results. The skillset of an Analytics Professional is also becoming more and more advanced, and we all need to constantly update ourselves to keep up with these emerging trends.
What are your thoughts on the recent surge in popularity of analytics around the world?
Over the past few years, there has been a shift from intuition-driven decision making to data-driven and fact-based decision making. Most successful organizations have made a conscious effort to shift to data-driven business practices. Because of the increasing availability of data, digitization and supporting IT infrastructure, this shift is taking place at a very quick pace. New age professionals are equipping themselves with the big data technology and advanced analytical skills, which and that is helping to further solidify the surge.
What are some of the most exciting projects that you’ve worked on? Is there anything that you’re working on right now that you can tell us about?
I’ve had the opportunity to participate in many end-to-end analytical life cycle projects. There was one where it starting with gathering business needs, all the way to executing analytics as well as implementing the analytical solution in collaboration with engineering and operational folks; and then also tracking the value of the analytical solutions over time.
In one of my past projects at a major bank, I observed that all lines of business were operating in a siloed fashion and fraud intelligence was not shared with each other. I realised that since we had so many overlapping customers among cards, banks, mortgages, auto loans etc., we needed to share and leverage already existing knowledge that we had about customers especially about their fraudulent behavior and look at it more holistically (to get a 360 degree view). I built a single repository of customer on-us fraud data from all the lines of business, and I built a logistic regression model on top of it. The model was built upon data like recency, severity, lob association, type of fraud etc to predict the probability of committing another fraud in next 9 months. The model and score was implemented in various places like auto marketing, card collection etc. where I had to influence very senior leaders about the value of data and the score while implementing in their space. This project ended up saving millions of dollars for the bank.
What are the main challenges facing the industry today, and how do you feel they can be dealt with going forward?
There is a shortage of analytics professionals who understand big data, open source technology, artificial intelligence and machine learning. Very often, data is scattered across many platforms, so there is a need for consistency, standardization and synchronization across disparate data sources. More and more training courses are being offered to train people on the latest technologies. I can also see an increasing number of women are studying more engineering/technology, computer science which will further solidify our analytical workforce. With the advanced analytical knowledge and skill-set, it will become easier to consolidate voluminous data in big data platform, and to derive meaningful insights out of it.
Have you, as a woman, faced any major challenges when building your career in analytics?
I did not experience any outright or subtle discrimination because of my gender. As a leader, if we let our work speak for us, it’s more than enough to get everyone on the same page irrespective of the gender. The industry today has evolved to a place that we are not discriminated any more by gender. The proverbial seat at the table is for both men and women based on their merit.
How has the industry changed for women in the last decade or so?
More and more women are nowadays taking up board and other senior positions in organizations, which was not so common a decade earlier. People are also becoming more receptive and welcoming to the idea of working under senior women leaders. The women networking groups are also becoming stronger, and they are helping each other even more to reach their full potential.
Is the analytics industry women-friendly on the whole? What do we need to do to ensure more women are able to break into the field?
We’re already seeing an increase in the number of women entering the industry. It’s starting from schools and colleges, where women are taking up more science courses and engineering and technology majors. We need to encourage women further right from their school and college days, provide adequate mentorship, more internship and training opportunities, as well as the right infrastructure (computers, softwares) so that they can train themselves even better to make a significant contribution in analytics field.
What advice would you give to aspiring analysts (especially women looking to break into analytics)?
An analytics career is one of the most popular career choices in today’s world; and for the same reason the competition is also fierce. It is important for all analytics professionals to constantly update their skillset. I would ask them to study science subjects well and to be curious, to seek mentorships as well as internships in this field and to network more i.e. to attend events, seminars and conferences to get to know people in the field. Lastly, they should also focus on improving their communication and soft skills as a big part of being a good analytics professional is how effectively one can convey a compelling story out of raw data and influence business leaders and stakeholders.