Take a seat at the table! Understand the big picture, and have confidence in your abilities.
You have been a woman leader in Data Science. What inspired you to enter this field?
I was always fascinated with Mathematics. Though I was the only female student in the Math class in high school , my father encouraged me to pursue my interests. I did my bachelors and masters in Math in India, and joined Cornell University to pursue a PhD in Mathematics too. As part of doing a minor in Operations Research, I took a course in Linear Programming in my first semester. That course helped me realize that Operations Research would enable me to do “Math with a purpose”. My first job after my PhD was as a visiting professor at UNC, Chapel Hill. After two years in academia I took a job with SAS institute as an OR Analyst. Over time my responsibilities grew within SAS. I led the team of OR Analysts, and then became the head of the Advanced Analytics R&D Division at SAS.
You have spent a number of years in data roles, leading teams in analyzing data, creating models, especially when groundbreaking innovations happened. How did you constantly upskill yourself?
According to me, one should always be open to learning new skills — be a lifelong learner; learning should be an ongoing process. My first job at SAS was to build scheduling software, for which I equipped myself on the job as I wasn’t an expert then in that specific area of OR. I did the same for learning many other areas of OR as well as graphics skills, and many other skill sets, especially as my role got bigger. While being an expert in all areas is difficult, it is important to learn how things work, as it helps you to understand your team’s work and lead them, as you go higher up the ladder. At SAS, as we entered the era of Big Data, we transitioned into a distributed computing environment, and all of us had to learn how to manipulate data that are distributed across multiple machines (CPUs) in an efficient way. It was a challenge for all of us, but it was exciting and kept us on our toes!
There have been so many applications of data science especially due to COVID, be it robot dog patrolling in Singapore, AR malls in South Korea, and so on. How do you envision the future of AI and ML? What are some areas that could leverage these advancements?
AI is the ability to execute highly scalable, extremely powerful and performant algorithms to enable automated data-driven decision making. All of us have been using Analytical tools for a while, and many organizations are using several machine learning techniques to improve their businesses. COVID19 and the related urgency has created demand for specific areas like the following: More resilient supply chains (Robustness Vs cost); Rapid retooling/pivoting to different products as needed; Increased automation in manufacturing, Increased use of wearables and remote monitoring to aid contact tracing, Dynamic changes to product portfolio, Automation in delivery and transportation systems and scores of other critical issues which need to be addressed.
You played a key role in the creation of the OR/AIML Centre of Excellence (to provide expert consulting to several Fortune 100 companies). What do you look for in potential candidates to be a part of it?
The OR/AIML Center of Excellence was established as a small group of expert consultants in OR, AI/ML who would work on some of the most challenging optimization and AI problems of our customers. To excel as a member of this group, a solid background in research is essential. So, the consultants we hired were expected to have the right blend of technical skills and the ability to work with existing consultants, to augment their expertise. The expert consulting skills include — understanding the business domain, and the big picture, analytical domain specific expertise (research level expertise and modelling expertise), communication skills, and ability to work in a team setting.
Along the same lines, talking about breaking into a career in data science, other than Data Scientist and Data Analyst roles, what other career paths are available for a data enthusiast. How does one build the path specific expertise?
There are multiple roles, and here a few to choose from depending on your aptitude and background.
- Business leader — Makes investment and hiring decisions for the team
- Analytics expert — Works on research and invents new algorithms if necessary to solve the problem(Generally an SME or Subject Matter Expert)
- Analytics modeler — Exploits analytical tools
- Database engineers and IT expert — Works on storage and updating data needed for the analytics
- Implementation expert — Deploys the analytical results, and monitors the applications
For analytics experts, advanced degrees in the chosen field are essential which needs to be constantly augmented with continued learning as research continues to advance. For others (like the business leaders) working with these experts, an acquaintance with the data models and analytical tools would be helpful. Journals like ORMS Today (which highlights some current problems solved with analytical expertise), online certifications and short courses on applications to get an overview of the analytics space, etc. are useful to provide them the background needed to work with the analytical experts.
How do you unplug?
My children pulled me to work-life balance. No matter how busy we were, we made sure to have dinner together. Also, I love taking vacations! Planning for vacations gives me joy and our trips were a fun way to spend time with the family.
What is one advice that you give for data enthusiasts, especially women? What are some skills women could develop to set a strong foot in the data industry.
Take a seat at the table! Understand the big picture, and have confidence in your abilities. Realize that you are here because your skills and expertise are valued. Use the innate skill of being empathetic — nurture your team and celebrate success. And finally, hire smart people — revel in their accomplishments.
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