Data science professionals are in high demand in 2018, especially in finance. And based on my research, this constitutes a superb opportunity for women and the finance sector.
How big is the demand for data scientists?
Demand for data scientists and advanced analysts is projected to grow 28% by 2020. Of that demand, 59% is expected to be in finance and insurance, professional services, and IT.
Data science and analytics (DSA) jobs account for 19% of all openings in the finance and insurance industries.
As Lindsay Hart, academic program coordinator at SAS Canada, an analytics, business intelligence, and data management company, told me:
“We hear on a weekly basis that customers are having a hard time filling vacancies on their data scientist teams. Students in the masters of analytics programs across the country are scooped up by top firms within a month of starting their program. Job offers are contingent upon students completing their analytics degree.
“International Data Corporation (IDC) says that worldwide revenues for big data and business analytics will grow from $130.1 billion in 2016 to more than $203 billion in 2020. Canada’s Big Data Consortium estimates Canada’s deep skills gap at as much as 19,000 and a gap of 150,000 in the analyst roles across industry. It’s clear that the demand for these skills is not diminishing. SAS Canada recognizes the need for talent and diversity in this field; not only do we invest in the education of new talent via our Academic Program, which reaches over 8,000 students across Canada, but we also recently launched a Women in Analytics network aimed at strengthening diversity in the analytics field.”
What is a data scientist?
The Harvard Business Review article “Data Scientist: The Sexiest Job of the 21st Century” defines a data scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.”
If that job description sounds a bit ambiguous, that’s because it is! And this is precisely where things get interesting for women.
“Data science isn’t magic and it’s not even a traditional science,” says Talia Borodin, the founder and CEO of Toronto-based data science company Amaro Science. “It’s just as much an art as it is a science, which means the variability in skills and ability is substantial.”
I had the opportunity to interview Borodin, an entrepreneur with a 15-year career in data. The following are her key points:
The finance of the future needs to be representative of women and minorities
“Finance has been an early adopter of Data Science practices which would have been practiced by quants (the finance term for those who rely on quantitative data) for decades. This long history of quant analysis has left the financial industry open to extreme bias. One area that’s been studied extensively is bias in credit scoring. By extension, the same type of bias analyzed in credit scoring is applicable across finance.
“Data Science is a new frontier and without good representation of women and minorities, we’ll be setting up data echo chambers that reflect financial success in the past, not the future. Consider how much of modern society is already designed by and for men. The algorithms being designed by data scientists today determine more than just what advertising you are exposed to. They will set interest and credit rates, and will approve or refuse loans, mortgages and insurance. They will determine which investment programs you are even eligible to participate in. They will try to predict the next great financial tech. They will inevitably be biased without very careful thought, intervention, and systematic bias testing. Having women be a part of that is critical.”
Women are well suited to the role of data scientist.
“More and more women are taking an interest in data science. I think it is now understood that data science roles pay better than analyst roles and as such many of the latter are trying to transition to the former. That said, many programming and bro-centric environments discourage women, who are in many ways, naturally suited for this role. Traditional schools of thought that women are better communicators in general is a huge advantage in the field of data science where understanding the problem is just as important as the execution.
“If women are interested in working for ‘social good’, there are many data science roles they can explore. Increasingly, not-for-profits are hiring data scientists for fundraising, targeting and experimental design. While the financial industry has had historically very little female representation in quants, the same barriers (biased hiring, bro-culture, work life balance, etc.) that are preventing women from desiring such careers is likely going to be a factor for data science as well. The good news is companies that are serious about having a well rounded and diverse team have options when recruiting. They can start with gender neutral job descriptions and blind testing for technical exams. The more women in the field, the more it will attract as it becomes a more female friendly environment.”
My prediction? We’ll soon be hiring and promoting based on data.
We need more female leaders in finance. Not only is it the right thing to do, it reduces risk and improves outcomes. But getting more female leaders isn’t just about growing the pipeline of candidates. We also have to improve the process through which talent is promoted to leadership positions. Historically, that path to promotion has been highly subjective and often biased against women and minorities.
Going forward, if companies make better use of data science, the rate of promotion will likely improve and probably in dramatic fashion. As Daphhe Khis, the CEO of WorldQuant University, observes, “In the near future, the double standard women face in the workplace will wane as adoption of data-driven decision making grows. Gone are the days of trashing a female executive for her ‘leadership skills’ without taking a closer look at her personal and financial-performance metrics.”
Do we need to find more women for our board? That’s simple. Consult the database of supremely well-qualified female board members.
The intersecting forces of data science, finance, and women are a game-changing combination. Data science will be the key to unlocking the door for women to the most powerful positions in finance.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
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