Data science or data analytics has emerged as the most lucrative field for young professionals in recent times. The World Economic Forum predicts that by 2022, those who choose careers in this field will be the most sought after, and the rise in data science requirements will create millions of jobs worldwide in the next five years.

Big data — which is the term applied to large sets of data harvested out of zillions of Internet searches, social media networks, retail experience and medical history — have turned into a goldmine for businesses and a data scientist or analyst is virtually a gold digger today. However, if one looks deep into this domain, there’s a key problem.

The under-representation of women in data science means increased chances of biased data-driven policies. Writer and feminist campaigner Carolina Criado-Pérez wrote about this bias in her book Invisible Women: Exposing Data Bias in a World Designed for Men. She offered fascinating examples. For instance, since automobile crash test dummies are designed based on men’s bodies for decades, women are more likely to sustain serious injuries in car accidents. Similarly, women’s exclusion from most medical trials have churned out drugs that are less safe or effective for them.

Once data sets get biased, it becomes difficult to fix. One way to cut down the bias is to increase gender diversity in test cases as well as among the professionals who work with data. Women’s inclusion in the field will surely add women’s perspective in data analysis.

Indrani Goswami is the director of analytics at Zip Co, a financial services company based in Sydney, Australia. She mentions that many hiring or recruitment algorithms tend to select male candidates because data sets are based on male candidates. “But as data scientists, we are cognitively trained to look at any data set without any bias,” she says.

Mathangi Sri is the head of data at GoFood, a food delivery app in Southeast Asia. In the past, she has built data science teams across large organisations such as Citibank, HSBC and GE, and tech start-ups such as 247.ai and PhonePe. She says, “Bias does get created in decision-making in a job. But having fewer representation of women in organisations is a bigger miss than losing a few features on the dataset.”

According to her, diverse perspectives about team, management and leadership strategies are lost if there are not enough women in a team. She adds, “Women are generally more organised, meticulous and multi-threaded, so they bring a different flavour to the organisation culture.”

Seeds of gender disparity are actually embedded in our social norms. Says Mathangi, “Contrary to the adage ‘catch them young’, data science lost them young.” She goes on to explain that our society does not encourage STEM (science, technology, engineering and mathematics) education for girls. “We still have stereotypes that compartmentalise fine arts to girls and STEM-based subjects to boys. Schools should encourage girls to experiment more and fail more,” she says.

According to Indrani, there’s a myth that only those good at maths can pursue a career in data science. “One of the best data scientists I have worked with had a major in psychology,” she says.

Consistent discouragement makes women such a thin minority in the field. Says Mathangi, “In my early career I have been the only woman in different time periods in my team.” According to her, women constitute barely 15 per cent of data analytics.

Geetha Joseph is a fresh graduate from Praxis Business School, which offers a nine-month full-time postgraduate programme in data science at its Calcutta and Bangalore campuses. She recently joined PricewaterhouseCoopers, a multinational firm, as a data scientist. She says, “Men to women ratio in my batch was 17:5. It’s obvious there is a gender gap.”

However, women have done well despite the gender gap. Geetha says, “Of the top five students in my batch, three are men and two women. Women are as competitive as men.”

Adds Ankita Ghosal, who is senior business intelligence engineer at Amazon and graduated from Praxis in 2017, “I have many female friends in the field doing exceptionally well.”

But both Mathangi and Indrani observe that things are changing now. According to Mathangi, certain templates of work culture — such as staying long hours in office, key decisions being taken during “smoke breaks” or casual parties after office hours by male colleagues — are changing, thanks to the pandemic. She says, “The pandemic has shown the power of ‘remote working’, in fact, even faceless working. Let us use this opportunity to provide openness and flexibility to women in this field.”

Not only are companies looking for more women data professionals, institutes are also taking initiatives to attract more women to their programmes.

Says Charanpreet Singh, founder and director of Praxis Business School Foundation, “While our data science programme is well-received and top-ranked in the country, Praxis is committed to encouraging and empowering more women to join the programme and build data science careers to improve the gender ratio in the profession.”

They have introduced a Women-In-Tech scholarship for women candidates, as they believe that “more women in tech is good for the institute, good for the data science domain and good for society”.