I recently caught up with Tammy Wang, VP of Data Science and Engineering at Riviera Partners to discuss her company’s partnering with the University of Virginia Economics department to build an AI-powered application that can help people successfully navigate STEM career paths, with a focus on developing a long-term career trajectory in tech. The solution has the potential to impact U.S. labor supply by addressing the increasing demand for technology talent, which has resulted from the rapid digitization and automation of the labor market. Riviera Partners is a leading executive placement firm that matches top engineering and product talent with tech innovators.
insideAI News: The National Science Foundation recently awarded you a large grant to use AI in a compelling new way to help women and other underrepresented groups break into STEM careers. Tell us about it.
Tammy Wang: I lead the data science team at Riviera Partners, a retained search firm specializing in placements of executive leadership in engineering, product, and design, and we have a front row view of how skewed supply and demand are in tech recruiting. There is a notable talent shortage in tech, and qualified candidates in software engineering or data science can walk away with multiple offers within a few weeks with large compensation packages. We also know that this hot talent market is remarkably unbalanced when it comes to gender, racial and socio-economic backgrounds. Only 26% of the software engineering workforce were women in 2018, only 2.2% of first generation graduates major in Computer Science, only 3.41% of software engineering degrees are awarded to Hispanics, and only 1.3% of degrees are awarded to people who identify as black or African American. In the big picture, the talent shortage and the narrow field of typical candidates is a warning signal that digital transformation efforts and innovation among US firms will be limited by a growing shortage of qualified talent. I believe companies across the US can have a bigger and better supply of tech talent if the STEM field can be more inclusive. Our team partnered with the University of Virginia Economics department to propose a project to build an AI-powered application that can help underrepresented groups navigate STEM careers, with a focus on developing long term success in tech fields. The project received a grant as part of the Convergence Accelerator awards from the National Science Foundation. We’re just starting this journey – and its exciting!
insideAI News: Why do you think an app like this was never created before?
Tammy Wang: Until recently, technology hadn’t advanced enough to amass and analyze the data that’s necessary to provide valuable recommended steps. The data set necessary to get to an accurate representation of detailed career paths is incredibly difficult to collect and integrate. Riviera is uniquely able to support this effort because it has nearly 20 years of history and data gathered from its efforts placing executives and individual contributors at companies. We have a horizontal view over a time series study that others lack. With this large volume of fine-tuned, clean data at our disposal, we’re able to deliver recommendations that are trustworthy. Also, understanding and recommending data-driven career steps for entire demographic groups is a difficult problem, and the process to do so is an obscure, exploratory area that requires a tight collaboration between industry and academia, and large-scale funding. It can be daunting to try to make all of these components align, which I think is why this type of project hasn’t been attempted until now, but with our data, technology and skill sets we were able to gain the necessary partnership and funding to make this opportunity a reality.
insideAI News: Why is an app necessary? Why can’t people in those under-represented groups just jump in to STEM careers on their own?
Tammy Wang: People often turn to mentors and personal networks when deciding which careers to move into, and to get guidance on how to get started in those careers. For people who fall within demographic groups that don’t already have a big foothold in tech, they often lack access to a tech community and mentor network. Within the population of demographics in the tech industry, females or those of low socio-economic-background make up just a small portion – which means that the number of tech-minded mentors they’ll turn to for career guidance is also small. This lack of STEM population trickles to the next generation. There is no easy way for these groups to gain an understanding of the trajectory of tech careers, without which they cannot make rational, metered choices. What we’re creating can serve as a “mentor” based on aggregated experience to help people better understand the IT trajectory, anticipate, then plan for the steps that are required to get on that trajectory.
insideAI News: Why do you think there still exists a gap between technology jobs and available talent to fill those jobs?
Tammy Wang: One big cause of the gap comes from the fact that many tech hiring managers are only tapping into a small slice of the overall population, which leaves them scrambling for talent. For instance: let’s say you don’t initially look at women because the proportion of women in tech leadership roles has historically been small. You’ve just cut your talent pool and it’ll get worse over time. Then, if you look only for certain academic backgrounds or pedigree schools, you slash your available talent pool even further. Now you’re down to 30% of the population. And, chances are the available talent population is even smaller still due to competition from other firms. For those who do make it through to get hired, they make up a very small percentage of the tech population. If the majority of their colleagues come from starkly different backgrounds, they may find it difficult to integrate with a company’s culture or bond as part of the group. If you start at a small number, then lose those employees, the resulting tech leadership pool is infinitely smaller. Proper training and guidance increases the initial candidate pool from 30% back to 100%.
insideAI News: What does the future hold for tech hiring?
Tammy Wang: Data is the defining factor. Approaching tech hiring as a data-driven exercise will break new ground and expand the pool of people companies consider as candidates, and how they assess those candidates. The volume and types of data that we’re now able to amass and analyze is rich, and companies will increasingly rely on the insights gleaned from that data to a) better predict how well candidates will thrive in different job roles and b) open up the floodgates to find more candidates, which will reduce the gap between supply and demand. I mentioned earlier that it’s detrimental to arbitrarily narrow down pools of candidates based on restrictions of socioeconomic position or gender simply because certain demographic segments have historically been less prominent in STEM careers. Those parameters will quickly become stereotypes that are proven incorrect. Similarly, hiring managers often make the same mistake when they arbitrarily assume that of the entire population of job seekers, only those who come from certain industries or learning institutions should be considered for tech roles.
Data helps companies take those blinders off to help people look away from the shiny objects, and see new pools of talent that were not previously considered to be in the running. Robust, clean, voluminous historical and real time data can point hiring teams to unexpected candidates who don’t come from a traditional tech background, but who have all the right qualities, traits and talent to succeed as a technology leader.
The insights of data can provide guidelines and foundations to people to think about possible opportunities and different paths to achieve goals. Data can also help candidates navigate through the thousands of open jobs and focus on their unique needs and aspirations to identify the right trajectory to develop long successful careers.
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