While times are changing for the better, the harsh fact remains that men have long dominated the tech space. The World Economic Forum Global Gender Gap Report 2021 states that women take up only 32% of the total workforce in data and AI. The dominance of men in leadership roles in business and technology and ways to include more and more women – often become topics of discussion in the media. 

Many times, we see young talented women being discouraged from taking up STEM-based studies. This has a ripple effect, and over generations, women move far and far away from pursuing STEM career options. A great way to motivate young girls to enter this space is by reading stories about women who have broken the glass ceiling and taken up challenging roles like data scientists, AI practitioners, and machine learning engineers. 

Today, we take a glance at Malini Jayaraman’s story. She is currently working as a data science lead at Shell, where her role is related to the commercial data science space and focuses on using analytics and statistics to help the business. Previously, she has worked at TCS and ICICI Bank and holds a Bachelors in Engineering and a Masters of Management in Business Analytics from Indian Institute of Science, Bangalore. 

Motivation to pursue STEM came from father

Jayaraman’s father had a business specialized in lab equipment services. Her 

interest in electronics was triggered when she tagged along with him to work. So much so, she even made circuit analysis her favourite lab exercise in school. Finally, she made up her mind to join instrumentation engineering post-school. 

Foray into data science was through a stroke of luck

While applying to colleges for her masters, Jayaraman came across a new program in the Indian Institute of Science (IISc) called Masters of Management. It had two electives – Technology Management and Business Analytics. She decided to choose the Masters of management course over the other ME offers in hand. During the first year, she got more exposure to Business Analytics. 

She says, “I also got an opportunity to intern with Honeywell and FLSmidth and work on data science projects during the summer break. This experience helped me understand how exciting it was to understand and analyze the business using numbers and analytics. I picked Business Analytics as my major after this. And, there has been no looking back.”

Hype around analytics

Just like everyone, Jayaraman has faced quite a few challenges in her career till now – the biggest of which is the hype around analytics and the expectations that it can solve any problem. Businesses is advertised that analytics projects are one-stop solutions for their problems. Sometimes, when such explorations fail, they lose their faith and interest in the analytics space.

On a daily basis, the other challenge she encounters is data. She states, “While it is true that we are increasingly collecting more and more data, it doesn’t necessarily translate into meaningful data. Data quality is still a major aspect that the industry is trying to tackle.”

Another major challenge for her has been the speed at which the industry is evolving and the overwhelming urge to keep pace with it in our daily work. Every day, there are new algorithms and use cases being identified.

“One thing that helped me here is to understand that while it is important to keep updated with the industry, it is equally important to make sure we don’t get caught up with the urge to force-fit the latest algorithms in our work,” adds Jayaraman.

What makes a good data scientist

After being in this industry for nearly a decade, Jayaraman has some words of advice. She feels a good data scientist should have these traits:

• Make sure to have the basics strong. Only when one understands how an algorithm works can one see the fitment into the problem statement. Applying models as black boxes will lead to failure in the long run.
• Understanding the business context.
• Doing due diligence on the data.
• Researching the analytics industry to come up with customized solutions will help data scientists succeed in their careers. 
• Being open to failures and learning from experiences will help data scientists go a long way.

Do not get influenced by the hype

For a fresher or an early-stage professional in data science, it is important to not get influenced by the hype. 

Jayaraman says, “Currently, there are multiple platforms to explore this field on your own. Make use of MOOC platforms in your personal time – self-exploration will be the best way to get started. Once you have an idea, explore other structured ways of learning that are abundantly available. You will then be in a better place to judge which course will suit you better.”

Societal expectations play a role in keeping women away from pursuing a STEM career

Two major reasons that keep more women from exploring such an amazing and rewarding field are – the belief that STEM is a difficult field for women and the societal expectations from a woman to take up a career path that prioritizes family over work.

But, the shift is happening. Jayaraman says, “Industry has started to actively combat this bias. In Shell, we believe in equal opportunity and strive to have a healthy gender ratio in the commercial data science team, enabling a community for the women employees to be mentored and fostered. Over the last few years, I have been seeing a change in the gender ratio at the workplace. More and more women are entering this field and rising into leadership roles. I hope that with increasing female role models in the data science field, the gender bias will become negligible in coming years.”

Men and women face different challenges

Recognizing that men and women face different challenges would be the first step in making sure we ensure equality, adds Jayaraman. An important piece of advice that her current manager and mentor gave her, which she swears by, is understanding the compromises we have to make in both career and family to have a healthy work-life balance. 

“From a technical community perspective, I feel that success stories of women data scientists will make more young women dispel their fears of failure. We get inspired when we see people similar to us scaling heights. Having a mentoring group for the students and young women by women leaders will help the community grow,” states Jayaraman.

Citizen data scientists and core data scientists will work together in the future

Data science will become a part of the business team as we advance in the future. “There will be two sets of data science – citizen data scientists helping businesses with day-to-day work with the help of analytics and core data scientists involved in advanced strategic projects,” concludes Jayaraman.