The Women in Tech Series: Beth Malloy

Expertise. Experience. Excellence.

Bias is typically something we project outward, but self-bias is also instrumental in propagating stereotypes. For example it’s been found that women presented with the jobs labeled “nursing” rather than “medical technician” are inclined to choose the first, because without realizing it, they align themselves with a female stereotype.”

Daniela Braga – founder and CEO of DefinedCrowd

This week, as we lead up to International Women’s Day, we’re spending time with some of the talented women working at DefinedCrowd, getting their insights and opinions on the gender imbalance among AI professionals (only 22% of professionals in the AI and data industry are female), the issue of gender bias in AI-enabled products, and what it’s been like working as a female in a traditionally male-dominated industry.

Today, we speak to VP of Finance, Beth Malloy; the woman in charge of all the finances at DefinedCrowd. From constructing operational models to accountancy, auditing and everything in between, Beth is a vital part of the C-suite team, and is a shining example of a woman excelling in a position not stereotypically associated with females (according to studies, women account for only 18 percent of finance jobs, less than in STEM fields). Here’s more with Beth.

Beth, why finance?

When I was in college, I became really interested in accountancy. I always liked mathematics – it’s something that came fairly easily to me and I just enjoyed it. I started out in public accounting and was soon working with large companies, collaborating with executives and C-suite members straight away. I had insight into the decision-making process, how companies are run, and how they are funded, and I really enjoyed it. It was incredibly stimulating for me.

Studies show only 22% of AI and data professionals are female. Why do you think such a disparity exists?

Typically, engineering and data science fields have been more male dominated. It may be that women feel these sorts of jobs aren’t suited for them, or perhaps they choose to raise families instead, or are not given the opportunity to access the studies required to enter such specialized fields. Hopefully that all changes; I see no reason why it shouldn’t. I think more and more women will see that data and AI is an interesting, challenging and profitable career choice.

Have you ever experienced gender discrimination in your career?

I’ve only ever experienced a few instances where I felt discriminated against because of my gender, but for the most part, I’ve been fairly lucky with my career. I did take a break to raise my family so I was out of the workforce for a while. I think that is certainly something women have to take into account far more than men do. Although, nowadays, a lot of men do choose to stay home to raise their families. I think this imbalance will even out. Hopefully men and women will share more of the responsibility of staying at home and raising a family, so it’s not just the women who are dealing with the impact of such a decision on their careers.

Have you faced any challenges as a woman working in the industry?

I think self-imposed challenges have been more of an issue for me, and I imagine, most women. If I am in a meeting with all men, for example, it’s easy to feel intimidated. I have to stand up for myself and know that I have as much to say as my male colleagues, that my thoughts and comments should carry as much weight. I think part of the issue is that women don’t tend to be as assertive as men, but I certainly think that will change as well.

Talking about gender, much has been said about gender bias in AI. Do you think this is a problem?

Gender bias in AI has been proven. Part of the problem is that a lot of the work done in AI has been done by particular groups. I think as we continue to develop and work on AI, these differences will start to narrow. But, I think initially, depending on who is doing it, there is going to naturally be some bias. There has certainly been a lot of attention given to the issue, so I do think it will change.

Why do you think addressing gender bias in AI is so important?

I think it is important because we want AI to impact everybody. The whole point of a lot of AI is to add efficiency and simplicity. When you are marketing or selling something, you want to connect to every conceivable person possible. There’s no point in leaving people out because you’re only hurting yourself. I think businesses are realizing this and making the necessary changes to include everybody.

What advice do you have for young women looking to forge a career in any traditionally “male” field?

Nowadays, it’s easy for people to jump from job to job, but even if you’re in a position you don’t love, try stick it out for a little while to at least get some good experience and learn to work with people. Learning how to work as part of a team is invaluable and will really help your career progress. Work hard to prove yourself and find ways to let your company know your value. But also have fun along the way. That’s important too.

Keen to read more from our #WomeninTechSeries?

Meet Xiaoting Wu – a software engineer.

Meet Elmira Hajimani – a machine learning engineer.