After graduating from Columbia University with a master’s degree in data science in February 2019, Aishwarya Srinivasan joined IBM’s Data Science Elite team, which helps companies develop the skills, methods, and tools for machine learning and AI models. Formed in 2016, the team has grown to nearly 100 data scientists, and has supported Sprint, Siemens, and other companies’ AI efforts. In a recent interview, Rob Thomas, the IBM executive who manages the team, referred to its members as “some of the best data scientists in the world.” Here, Srinivasan discusses her role on a data science team working at the forefront of artificial intelligence.

Tell us more about the Data Science Elite team and its mission. 

The Data Science Elite team was formed with the aim of helping various industries  promote and adopt machine learning and artificial intelligence. The team is diverse, consisting of data science and AI experts who have subject-matter expertise as well as the skills to solve specific industry-based use cases. We work alongside clients to help them adopt machine learning in their businesses and make the most of the technology.

What is it like to be a part of such a team?

Being a part of the team is like having your passion turned into a profession. As a computer science major in undergraduate school, I would never have thought I’d be able to work with financial firms, government organizations, and the healthcare industry. Each client we are involved with gives me deep insights into how data science can be leveraged to do things that not too long ago were considered almost impossible. We not only work to deliver business value in our engagements with our clients, but also try to build solutions that can benefit the human community as a whole.

Does the team ever work on pro bono projects?

We don’t charge for the work delivered; there isn’t any obligation to pay. During the engagement, we use all IBM niche products and services. We believe our clients will find value in IBM products and services such as IBM Cloud, Watson Studio, Watson Machine Learning services, Cloud Pak for Data, and continue to use them after our engagements with them.

Which data science skills do you use during your engagements with clients? 

The team engages with clients all across industries and we work in about 180 countries around the globe. We have been involved in projects related to recommendation systems, text classification, call-record analyzing, automation, optimization, time-series prediction, and anomaly detection. The team primarily focuses on Python and R as programming languages, in parallel with using IBM technology built by IBM research teams. If necessary, we can also deliver end-to-end solutions including visualization, productionalization, and dashboards.

Outside of work, you help girls cultivate interests in STEM subjects and careers. What motivates you to do that?

There’s a prevailing stereotype that says girls are less capable than boys of learning technology and it is absolutely not true: Aptitude is independent of gender. We need to make both men and women realize that engineering or data science is just another subject, and irrespective of your gender if you are interested in it and are willing to work on building your skills, you will excel in it. I am an ambassador for Women in Data Science, a nonprofit that supports girls and women in the field. I am an advocate for women in technology, women in STEM and women in data science. I mentor young girls who show an interest to enter the fields of computer science and machine learning. I help them realize their potential and show them the opportunities in the fields and what they can achieve professionally.

How did the master’s program at DSI prepare you for these opportunities? 

The program not only prepared me with technical skills and aptitude, but it also gave me an opportunity to connect with the best minds around the world, including my classmates, professors, and researchers. DSI gave me much more than I could have ever expected from a master’s degree program. It gave me a wider perspective, both in terms of applied data science and research.

What advice would you offer DSI students regarding their studies?

I suggest that current students broaden their vision of data science by not only focusing on academics, but keeping themselves involved in other data science community meetups and activities. They should be open to the trending news about various industries. This gives us ideas on how we can utilize the power of data science and machine learning in new arenas. Keep following the conferences to know about active research in the field and read, read, read as much as you can.

— Robert Florida