How to Recruit Women in Technology
One thing we’ve seen companies struggle with is “how do we recruit more female engineers?” At about the same time in planning for their open roles, hiring managers are debating whether to add that “must have computer science degree” requirement on the job description. Wanting both creates an interesting problem - if we look at the U.S. Department of Education data, we can see the share of women with undergraduate degrees in engineering has declined since the mid-1980s. The graph below shows women as a percent of the total bachelor’s degrees for that year -
So if a firm is looking for someone with a degree in computer science and 5-10 years of experience, the graph tells us that females made up less than 20% of all engineering degrees for all of those years. Looking at actual numbers – using 2011 as an example, there were about 8,000 women who received computer science degrees; and over 35,000 men. Companies requiring the degree may be making an already small pool of candidates even smaller.
As a result, teams may want to look at different educational backgrounds in order to achieve a higher gender balance or adjust strategy in other ways. This could include sourcing from a hacker school, utilizing job boards that are dedicated to diversity in tech, or attending a Grace Hopper event. For a longer term benefit, your team can also seek to build their network by getting involved with organizations that support the women-in-tech community. Extensively long lists like this are a great start.
The ideas above may help drive more female candidates to look at your job openings, but if you want them to both apply to your job and be successful in the interview, you’ll want to have a structure in place to remove bias from the process. How to do this?
- Start with the job description. There’s plenty of research out there showing that the word choice in your job description affects who will apply to your job. Using more gender neutral words increases the chances you’ll attract a more diverse pool of candidates. Products like Textio can automate this process, and some examples of how to think about and re-word your descriptions can be found here.
- Be Aware of Gender Bias. A great (if not alarming) recent study looked at over a million GitHub users and found that
“… code written by women was accepted 78.6% of the time and the code written by men was accepted 74.6% of the time. But when female coders indicated their gender, their code was less likely to be accepted: their acceptance rate plummeted to 62.5%. The findings suggest that women coders face a persistent gender bias.”
In other words, just having a female name on a resume may (unconsciously) lead your interview team to believe the candidate will be a less competent coder than a male candidate. An option to explore is adopting a gender blind hiring process, which leads us to -
- Update Your Hiring Process. Airbnb recently did a case study on themselves as they realized there might be gender bias in their hiring process. They experimented by transforming certain stages of their interview process, and were able to grow the percentage of females on their data science team from 15% to 30%. One of the things they did was to remove potential gender bias from how they graded a take home test, a standard part of their data science interview.
“[…] we removed access to candidate names and implemented a binary scoring system for the challenge, tracking whether candidates did or did not do certain tasks, in an effort to make ratings clearer and more objective.”
This is a great example of a way to apply the findings from the GitHub study referenced above.
We’d love to hear from you – if you have implemented any strategies (large or small) that have worked for you, shoot me an email at email@example.com. I’ll be writing a follow-up post on what’s been working.