How New York based startup SquarePeg is using AI “to help save time and improve quality of hire” in the HR industry- Interview with CEO Claire McTaggart
AI has often been represented as intelligence with a cold robotic touch. However, the truth is that AI has a lot to offer to allow us to tap into our human side, and can help us with many other aspects of our work. A recent article in Raconteur advocated that “Adopting artificial intelligence within human resources will speed up processes and create more time for the truly human side of the job.” And this statement does make sense. As AI becomes better at facilitating the more tedious and time-consuming jobs, the human worker can focus more on the areas that AI would struggle to fulfill, the human element of HR.
To get a better understanding of how AI might impact this industry we spoke with Claire McTaggart, Founder, and CEO of SquarePeg, an HR and recruitment platform that matches pre-vetted, high-quality candidates via predictive analytics, psychometric data and artificial intelligence to match the company’s hiring and cultural requirements.
I understand SquarePeg uses AI, Predictive Analysis and psychometric data for potential candidates. Can you briefly explain what role each of these factors plays in the process and how they are used?
SquarePeg uses psychometric data to measure 19 of the most important workplace personality traits – so that we can improve matching beyond just resume data. We look at attributes such as detail orientation, adaptability, and perseverance, and measure each candidate on our platform with high validity and reliability. Employers using SquarePeg to hire identify the traits required for the job – for example, an organized and proactive manager who works well in high stress or chaotic environments. This allows us to to help source and match the right talent pool for a given job, beyond what other platforms can do.
Our predictive analytics are a result of the matching algorithm that our data science team has been working on for the past year. We parse and analyze resumes across 5 different categories (experience, skills, industry, role, education) and can score the relevance of each to the job requirements, going well beyond simple keyword matching. This combined with the data on personality and environment fit allows us to produce highly relevant analytics for our clients. Because this process is data-driven, and not just based on heuristics, it has the result of producing a more diverse pipeline of candidates and reducing bias.
We introduced machine learning into our matching algorithms and prediction models to help save time and improve quality of hire. As each candidate or employer connects or passes on a job match, our algorithm identifies the trends and patterns in the hundreds of data points behind each match, and adjusts. So if skills matter more for the digital marketing role, and personality attributes for the sales role, our algorithm can learn weightings and adjust in real time. The main concern with machine learning is scaling bad decisions and incurring bias, so we have put checks and balances to ensure that female or diverse candidates are never at a disadvantage.
Do you believe that AI will ever reach a level of ability where it will be able to manage and oversee the whole process from start to finish of hiring a candidate for a position?
Humans should always be involved in the hiring process, because ultimately we are the ones who oversee and train the algorithms that help make our jobs easier. AI should help save time by reducing the amount of search, filtering, messaging, connecting and processing. A human should always do the high value work, which is mainly taking the insights that are produced at each stage of the hire, and driving business decisions. As AI starts to enter HR in a meaningful way, HR will ultimately become a more strategic and proactive function.
We haven’t seen this with any of our clients, but that’s because we focus on the quality of the match, and not the quantity. A job seeker on a job board might submit resumes to a host of companies, and skip any of the interviews of those that are the least relevant to their preferences. Because we measure desired benefits, company culture, salary, environment fit, and a host of other metrics, we only send job seekers positions that aspirationally meet what they are looking for in their next position. Most of our job seekers are passive candidates, meaning they are employed and not looking on job boards, but would move for the right opportunity. By matching them with only relevant options, and then checking their interest through our platform, we can reduce some of the behaviors you are referring to.
Can you give an example of a company or case study that has really benefited from SquarePeg?
SquarePeg is size agnostic – meaning we work with Fortune 100’s as well as 10 person startups to help them hire their non-tech talent (sales, marketing, ops, etc.) One of my favorite examples comes from a VC who came to us trying to fill an analyst/ customer experience role, and had gone through 3-4 people in a matter of months, mostly due to bad fit. We were able to identify a strong pool of candidates for the role that had the appetite, disposition, and organizational skills for the role. We also used our match data to provide our client with behavioral questions that were far more insightful than a typical resume review.
Who do you consider to be your main competitors and how do you differentiate yourself from them?
We put our competitors into two distinct buckets – candidate sourcing solutions, and assessment tools. On the sourcing side, there are a growing number of job boards and recruiting companies focused on high-volume low-quality matching. You can post a role to any of these, and see hundreds of candidates, but many won’t be relevant for the role and a lot of time is wasted. The data they collect is not comprehensive, and therefore the quality of matching is limited. The recruiters come with a big fee, but lack the data and insights around a match that should be expected by every hiring manager in this day and age.
The second bucket is assessment companies, which act as an additional step in your recruitment process (not great for candidate experience), and rely on the company to source applicants for them to assess. The concern here is that the assessment companies don’t source the candidates or engage in resume matching, so in essence they are creating more work for both the employer and the job seeker, without controlling the quality of the applicant pool.
What is the main focus for SquarePeg over the next 6 months?
We have some really exciting developments we are working on, all related to how we measure, match, and report data. We are improving the measurement techniques of our assessments to be able to improve accuracy while reducing time, and developing a suite of analytics for both job seekers and employers. This means we will equip recruiters and hiring managers with the data they need to make strategic decisions, which we can do because we touch both sides of the market. I think once we integrate with the majority of ATS providers (particularly with custom integrations that consider our valuable data), SquarePeg will be able to offer a more seamless experience to companies using us to hire.
Disclosure: This article includes a client of an Espacio portfolio company