In honor of Women’s History Month, we’re shining a spotlight on the incredible women of Deloitte Digital. Discover how three female data scientists are making a difference every day.
“AI Specialist” was recently reported as the number one emerging job title of 2020, but only 28 percent of the science and engineering workforce are women. As our world becomes more driven and informed by technology, there is a clear need to not only increase diversity in the field but to also embrace inclusion to help ensure emerging technology is reflective of all human experiences.
Jaichitra Balakrishnan, senior data scientist; Deepshikha Gandhi, Senior Data DevOps Engineer; and Claire Jiaying Wu, data scientist, build data and technology solutions that help elevate the human experience for the clients that Hux by Deloitte Digital serves. We asked them to share their unique perspectives on why diversity and inclusion matter, how it serves to elevate the human experience, and what opportunities there are for women in technology-driven fields right now.
Claire
Jiaying Wu
I had gained a lot of work experience in the marketing field, meanwhile, I’d always wanted to drive marketing impact in a more data-driven and scientific way. When I was in my late twenties, I realized it was the time to go back to school and upgrade my whole knowledge system. Fortunately, I made it through and stepped into a field I was always curious about.
Deepshikha
Gandhi
As a little kid, I was always fascinated to know the whys and hows. So when I was ten-years old, I started reading about the Big Bang Theory and evolution, and that passion has stayed with me throughout my whole life. Working in big data has given me the opportunity to continue this quest to go beyond the numbers, and dive into discovering the whys and hows behind them.
Jaichitra
Balakrishnan
A challenge for me was being introverted, especially when communicating with clients. I had to find my footing and become more confident in speaking up and making sure my voice is heard.
Deepshikha
Gandhi
I had to build my career from zero experience—it was through the need to re-invent myself by keeping the pursuit of knowledge evergreen that I learned to ask questions, and to not be intimidated by new buzz words. It helped to grow my confidence, no matter the setting.
Deepshikha
Gandhi
The whole point of AI is to make algorithms more intelligent and mitigate bias. That cannot be done effectively without women.
Jaichitra
Balakrishnan
I completely agree. Diverse teams embolden women to feel more represented in their work. I think that making an effort to bring in people from different walks of life helps reduce bias from work.
Deepshikha
Gandhi
Different people bring different perspectives to the table—a different way of solving problems and an emotional intelligence inspired by their life experiences. With diverse team, I think we can bring all our differences together and use them to build the next-generation technology stacks.
Jaichitra
Balakrishnan
I’ll also point out that diversity isn’t just about gender or race, but also about less obvious dynamics, such as accessibility and socioeconomic status. For example, I’m dyslexic which leads me to seeing value in using technology like signal processing to help anyone who communicates differently. I’m inspired and motivated by the ways companies use technology to help dyslexic students or other children with a deviated mental growth plan. Without an understanding of these experiences and these possibilities, technology cannot fully reach its potential.
Claire
Jiaying Wu
First and foremost, it’s not solely about gender, but being suited for the job. Specifically, being in the data science field in general requires a person to be very curious, logical, and detail-oriented.
Jaichitra
Balakrishnan
I see healthcare and emotional AI as particular areas that could use a gender diverse approach. For example, in emotional AI—a domain that’s growing quickly—it’s important to understand and consider how different cohorts process data, react to different situations, etc.
Claire
Jiaying Wu
Take time to figure out whether you really have a passion for the field or not. Try to put your passions and skills into practice through online courses and/or open source competitions.
Deepshikha
Gandhi
Always, always, always ask questions. I remember, when I first started out, being very intimidated by team leaders and higher ups. They’d use acronyms I wasn’t familiar with, and rather than ask them to clarify, I would go home and look up everything after. Don’t be like me! Remember that there are no stupid questions, and that you can’t learn if you’re not asking.
Jaichitra, Deepshikha, and Claire agree that the opportunities in technology are limitless. Elevating the human experience involves a diverse understanding of humanity, and as such, diversifying the STEM field is a priority at Hux by Deloitte Digital.
Learn more about careers at Hux by Deloitte Digital and the ways that Deloitte is championing women in data science and analytics.
Jaichitra Balakrishnan is a senior data scientist from India who holds master’s degrees in computer science and machine learning, and has worked in the engineering field for 10 years. Technology is her current passion, but she’s always aimed big; as a child, she wanted to be an astronaut, having been inspired by Kalpana Chawla, an Indian-American female astronaut and role model. She resides in New York.
Deepshikha Gandhi is a senior Data DevOps Engineer from India now living in Detroit and focused on big data. Since her earliest memories, she has always asked “why” and “how” questions to better understand the world around her. She has been interested in science and technology since the age of 10, and first moved to the US in 2012 to pursue a master’s degree at the Georgia Institute of Technology.
Claire Jiaying Wu is a data scientist living in New York who left her first career in the marketing analytics field to attend a Master’s of Science program focused on applied analytics at Columbia University. She is passionate about how business reasoning and scientific reasoning converge to drive decisions.
