How Atlassian Uses Data to Attract More Qualified & Diverse Candidates

In her time as Global Head of Diversity and Inclusion at Atlassian, Aubrey Blanche has had some impressive accomplishments, including boosting the number of female technical hires by 80%.

We recently partnered with Aubrey on a webinar to discuss how Atlassian uses data to attract more qualified and diverse candidates. Learn some of the strategies Atlassian applied during various stages of the hiring process to become more inclusive and welcoming to candidates from diverse backgrounds.

We’ll share the highlights below, or you can catch the full recording of the webinar here.

The starting point

Two years ago, Atlassian had 11.5% women in technical roles. After realizing that women and people from marginalized groups weren’t well represented, they made two assessments: first, they weren’t recruiting in the right places, and second, they weren’t optimizing their recruiting funnels.

Changes to the recruiting process

In order to create a more diverse engineering team, Atlassian made a number of changes. We share some of the most impactful ones below. If you’d like to explore any of these in more detail, Atlassian offers a detailed explanation on their company diversity page.

  • Standardized interviews

Research from Google shows that structured interviews are one of the best ways to predict candidate performance. This means that rather than letting interviewers ask candidates whatever they want, everyone adheres to a consistent set of questions. Atlassian also ensured that they defined exactly which technical skills candidates needed to possess and helped interviewers understand how to evaluate candidates on these skills.

  • Unconscious bias training

In order to get all employees involved in improving the interview and hiring process, Atlassian offered unconscious bias training. Aubrey emphasizes that this is not merely educational—these strategic sessions help interviewers interrupt their own irrationalities. And it’s worked! She says that the training has given people actionable advice and an increased awareness of how to reduce bias in the hiring process.

  • Down with “culture fit”

The “culture fit” interview is a staple of many modern companies, but the trouble is that this term “is often poorly defined,” says Aubrey. That’s why it’s important to be very clear about how your company defines this term and create a rubric that helps interviewers assess candidates. Atlassian has shifted away from “culture fit” and instead interviews for a well-defined “values fit,” gauging whether candidates have empathy, are frank, etc. rather than judging whether the interviewer would want to have a beer with them or some other arbitrary personality test.

  • Being authentic about equal opportunities

When Atlassian first launched a graduate program for entry-level technical roles they had 0 female applicants. Aubrey says, “It’s hard to hire someone who doesn’t apply!” So Atlassian looked at each part of the candidate funnel to see who was coming to each stage and what the distribution looked like. She explains that as part of the initial screening questions, Atlassian included the standard US legal language about EEOC along with questions that asked candidates to voluntarily disclose their gender and ethnic background. At this time, only about 20% of people would voluntarily answer those questions.

So Atlassian switched to language that was more authentic and explained that the data being collected would help Atlassian provide more equitable opportunities and would never influence hiring decisions. By making these changes, the response rate to EEOC questions jumped to 51%.

  • Reinventing job ads by bringing data to writing

The way that a job ad is written can have a big impact on who ends up applying to that job. In fact, Aubrey shares that job listings with equal opportunity language fill 10% faster on average across all demographic groups. Atlassian partnered with Textio, a company that compares the writing of job descriptions to hiring outcomes for more than 300 million job posts, to identify words that had subtly gendered tones to them. For example, they learned that words like “ninja” and “rockstar” are words that don’t appeal to women and people from underrepresented backgrounds. They also made sure to write job descriptions with inclusive language, featuring words like “diversity and inclusion” and “diverse teams.”

The results

Within a year, the percentage of female technical graduates in the Sydney class jumped from about 10% to 17%. The Sydney and US technical entry-level roles are now 57% women. And what’s really exciting about this change is that the most gender-balanced class is also the best-performing class to date. This is in line with Atlassian’s philosophy that by creating a more diverse company, they’re actually raising the bar.

Aubrey’s presentation taught us that data can help you better understand where your company currently stands with diversity, e.g. are candidates from diverse backgrounds applying but not making it through the interview process? Or are they being scared away before they even apply? You can use data to choose where to focus your efforts—and to measure your results.

If you’d like to explore any of these topics more thoroughly, be sure to check out the full recorded version of the webinar here.

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