I was a Data Science Manager when I was asked to double the size of my team. The only way I wanted to do it is by hiring a diverse team. That meant diversity in gender, nationality, academic background (PhD vs MSc), or even major (maths, economics, physics, etc.). At that time, we were 2 females out of 5 people in total and I wanted to make sure we maintained (if increasing wasn’t possible) this ratio of at least 40% females in my team.
First advice: own it. I strongly believe that diverse teams bring a better work atmosphere and better results and that women’s abilities are often unconsciously underestimated and overlooked. I’ve seen many of my male peers being promoted without the proper credentials and yet I only hear how higher a diverse team would “lower the bar”. I even heard that “she was hired because she was a woman and not because she had the skills” – even though statistics show that women definitely get discriminated against when they do have the skills. So be prepared with some hard facts on gender discrimination and how by fostering a more diverse workplace, we actually raise the bar and make the most of all available talent instead of only tapping into the over-represented group.
What we first did is to modify our job ad: we would remove egocentric phrasing such as “we are the best of the best of the best” and add more content towards what it is we are actually doing. Why does this contribute to hiring more diverse team? Simply because not everyone identifies with egocentric statements nor wants to join a team that has a boastful atmosphere. On the contrary, providing more information on what the team is doing can attract talent that share interest in the team’s goals. We passed the job ad through a gender lens analysis. We reflected on our key requirements and moved the rest as “nice to have”. We made it public that the team had a 40%-60% female-to-male ratio and that we wanted to attract talent from underrepresented groups. We also added a note that we’d like to review all candidates, even the ones that have only 60% of the requirements.
Then we published our job ad, not only to the regular channels but targeted forums and websites that have a large audience of women in tech such as PyLadies or Redi School of Integration etc. I also mentioned to my network that I was hiring and most people who knew me contacted their network, too. Since I’ve been known to advocate for women in my company, it also attracted female talent that wanted a fair chance to succeed and were looking forward to reporting to a woman (which I never experienced myself).
Before the interview, we decided to be open to different profiles. For example, if your team works on customer behaviour, a person with a background in psychology could be quite interesting. We set the criteria for hiring in advance, to avoid being biased during the interview. It was important to have a reproducible interview process that would be the same for everyone. We also checked in with our own biases through training and learning more about them.
During the interview, we need to check in with our biases, for example, if the candidate comes from a prestigious university or if they speak somewhat faster than we’re used to etc. Make sure to only evaluate against the pre-defined criteria and to give everyone a fair chance. When I received French-speaking candidates, I would still interview them in English so that the experience would be as similar as with the other candidates. If the candidate speaks faster or with an accent that is more difficult to understand, ask them to speak slower and move on.
If you want to attract female talent, it is also essential that you show diversity during the interview process, especially in tech roles that usually have 4-6 interviews: get at least one woman to interview the candidate.
Last but not least, make the person feel at ease and empathise with the fact that females have faced more barriers to get there (lack of role models, lack of representation in the media that could have led to a lower sense of belonging / legitimation). Do not hesitate to use prompts such as “you’re doing well so far, take your time”, “where would you start?”, “let’s brainstorm together, what is the first thing that comes to your mind?” etc. especially when the person gets a huge blank and gets “frozen” before you.
After the interview, challenge others on their biases e.g. the typical “not technical enough”. Make sure to have objective criteria to evaluate the candidates and challenge others if they show clear biases such as “their resume is impressive”. Keep a benchmark handy of candidates with X education and X experience, and take note of which titles and salaries they were offered. If you notice a gap regarding salaries or titles that are being proposed by the hiring team i.e. seniority levels, make sure to challenge the suggestion, even involving HR in the process. It is not uncommon to have female candidates with the exact same education and experience being offered lower titles or salaries – having a benchmark of people in the company can be a good way to combat and correct bias. It is of course easier if the HR data are available to you.
Using all these different strategies, we managed to keep a 40-60% ratio in our team. And this was only the start!
We still needed to build an inclusive and fair workplace. Watch out for discrimination and mistreatment: provide equal opportunities (not only for promotions but for speaking out, for exposure to stakeholders, for working on key projects etc.), use inclusive language, keep an open ear to women if they need to share their stories, advocate for women in the leadership rounds, challenge promotions if men are promoted at a higher rate, empower women to speak again when they’ve been interrupted, etc.
These are my two cents on how to hire a diverse team. I hope that you find it useful and that you are able to reflect on how you can incorporate these pieces of advice in your day-to-day work life. If you would like to discuss more tips on diverse hiring practices, feel free to DM me on Elpha or contact me on LinkedIn!
I am a data & ML expert with 10 years of experience and 5+ years of experience leading and scaling international teams. Additionally, I am a professional coach for females in tech and a diversity & inclusion consultant, guiding tech companies to close the gender gap.