Blog post

Building a diverse workforce at OfferFit

Building a diverse workforce at OfferFit
Written byMatt Kisling & Nathaniel Rounds
Published11 Jul 2024

The tech sector continues to struggle to diversify its workforce. Women hold only 27% of tech jobs (including holding only 16% of tech management positions), and Black and Hispanic workers represent only 8% and 7% of the tech workforce, respectively, far below their overall labor market presence. 

At OfferFit, we believe that the strongest team we can build is necessarily a diverse team. We’ve grown significantly in the past two years, and we’ve set the intention of becoming more diverse and inclusive as we grow. In 2022, about 38% of new hires were members of underrepresented groups (URG). In 2023, that number was closer to 50%. These numbers reflect our ongoing commitment to inclusive hiring and the progress we are making towards building a more diverse workforce.

While we’ve highlighted the broader diversity gap in tech hiring, this blog will focus primarily on the starting point we selected – addressing the gender gap. Our goal is to provide insights and strategies we’ve used to bridge this gap, and to foster an inclusive workplace where everyone feels valued, respected, and empowered to succeed.

You can’t hire candidates who don’t apply

When we first began to investigate our hiring outcomes in a systematic way, we found good news and bad news. The good news was that members of underrepresented groups passed from each stage of the hiring process to the next at similar rates as the overall candidate pool. For example, we learned that women and men who reached the resume screening stage for technical roles were about as likely to interview, and about as likely to get job offers. That is, men and women progressed through our “hiring funnel” for technical roles at similar rates. The bad news: we were receiving 4 times as many applications for these roles from men.

In other words, we didn’t have a problem hiring women into software engineering and data science roles – we had a problem getting them to apply for these roles in the first place. As any marketer could tell you, our problem was “top of funnel.” We implemented a number of different strategies to build a more diverse pipeline.

  • We reached out directly to members of underrepresented groups on LinkedIn to encourage them to apply for open roles. (We focus our “cold outreach” efforts on LinkedIn.)

  • We posted job openings in user groups, forums, and slack communities that cater to women in tech, e.g., Women in Machine Learning, Women in Data Science, PyLadies.

  • We direct prospects to public resources, such as our Glassdoor page, so they can read for themselves what our team says about inclusivity at OfferFit.

These strategies help with awareness – from there we need prospects to actually apply for the job. Research shows that men are much more likely than women to decide that they meet the requirements of a role, and we want our applicants to know that we know it. That’s why the following message appears at the top of  every OfferFit job description:

Data shows that men on average apply for a role if they meet 6/10 requirements while women often only do so if it’s 10/10.  We work hard to be clear and specific about what our roles require, and we encourage you to apply even if you don’t check all the boxes!  Applying gives you the opportunity to be considered and we look forward to reviewing your application!

Anecdotally, we know that women have decided to apply for tech roles at OfferFit specifically because they read this message. And we know these strategies have begun to chip away at our top-of-funnel gender gap – the ratio of male to female applicants from technical roles has shrunk from 4:1 to less than 3:1. Great progress, but we know we still have a long way to go.

Structure reduces bias

The research is very clear: structured interviews are typically fairer for candidates and more effective for employers. Columbia University observes that unstructured interviews persist despite a “vast literature suggesting that they have little validity.” Meanwhile, 70% of applicants prefer structured interviews, and 86% of hiring managers believe they reduce bias.

At OfferFit, we typically structure our hiring process in three ways:

  1. During the resume review stage, we typically align on specific criteria to determine which resumes move forward. We define what “relevant” means for the role and then evaluate candidates based on years of relevant experience, expertise in relevant areas, educational background, and other pertinent qualifications. We intentionally maintain some flexibility in this stage, because we recognize that candidates are not professional resume writers. We usually give the benefit of the doubt to candidates whose experience closely aligns with our requirements, even if their resumes aren't a perfect match. This approach helps ensure that we consider a broad and diverse pool of candidates.

  2. For each role, we develop a comprehensive interview script and scoring rubric, designed to assess the specific skills and competencies required for that role.  Each interviewer typically asks the same set of questions to every candidate, ensuring consistency. This approach generally helps minimize subjectivity and bias, as candidates are scored based on the predefined rubric. Our interviewing team receives thorough training on how to use the interview material, including shadowing experienced interviewers. These sessions help ensure that all interviewers are generally consistent in how they ask questions and apply the rubric. 

  3. During interviews, our team scores the candidate's responses against the rubric. This process helps ensure that all aspects of the interview are considered in the evaluation process and that no important details are overlooked. Interviewers typically do not share notes until after interviews are completed, to avoid biasing each other's scores. Hiring decisions are then made with input from all of the interviewers, and typically involve discussions among the interviewing team and members of our senior leadership team where scoring is reviewed to ensure consistent calibration in our decisions. This collaborative approach ensures that multiple perspectives are considered while remaining data-driven, promoting fairness and reducing the potential for individual biases to influence the decision. 

Of course, no such process is perfect, but we believe it’s important not to let perfect be an enemy of the good. Structured interviews are an important step in the right direction.

Iterate and improve

Finally, building a diverse workforce is not a “set it and forget it” process. We continuously assess our hiring efforts – we review all hiring metrics on a quarterly  – and sometimes weekly! – basis to ensure we maintain our inclusive hiring standards. We track the progress of different underrepresented groups throughout various stages of the hiring process, and our recruiting team and leadership will drill down to identify  and implement process improvements. This iterative approach helps us refine our techniques and maintain a fair hiring process. We hope the strategies outlined in this post can help other companies put these values into practice.

Recruitment isn’t just for recruiters

If you read this blog post and thought, “This sounds like a lot of work!” – you’re right! It might seem impossible for a small recruiting team to put this structure in place. In reality, our entire team is involved. Recruitment and citizenship shows up on our performance evaluations for all employees because we believe we all need to play a part ensuring we maintain a high bar – for talent, inclusivity, and rigor. And the investment has paid off: we continually see in our internal surveys that “great people” are one of the top reasons people would recommend their friends work at OfferFit.

Matt Kisling is a Sr. Recruiting Manager at OfferFit with 16 years of experience in recruiting. Before joining OfferFit, Matt served as a Senior Delivery Manager at Apex Systems, a leading global technology services firm. He holds a B.S. from York College of Pennsylvania.

Nathaniel Rounds writes about AI and machine learning for nontechnical audiences. Before joining OfferFit as Product Marketer, he spent 10 years designing and building SaaS products, with an emphasis on educational content and user research. He holds a PhD in mathematics from Stony Brook University.


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