Interview: Tammy Wang, VP of Data Science and Engineering on Approaching Tech Hiring as a Data-Driven Exercise

on 11 | 13 | 2019

Tammy Wang,  our VP of Data Science and Engineering connected with InsideBigData to discuss her team’s work 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. We invite you to learn more in the excerpt below, or click through to read the full Q&A on InsideBigData. 

insideBIGDATA: 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 it’s exciting!

insideBIGDATA: 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.

Read the full Q&A at InsideBigData, Interview: Tammy Wang, VP of Data Science and Engineering on Approaching Tech Hiring as a Data-Driven Exercise or contact Tammy to learn more.