Beyond the Keyword Search: Finding Job Candidates with CV2Vec
A major challenge for HR teams is finding, interviewing, and onboarding job candidates. At Talla, we are building intelligent assistants that employ deep learning to help offload some of the tedious and time-consuming parts of this workload. This talk focuses on CV2Vec, a set of experiments we’ve done on the candidate sourcing side of this process. By training neural models to map CV and resume documents into a dense vector representation, we are able to perform candidate searches on more than just keywords. We can find candidates that are most similar to a reference person or the job ad itself, cluster people together and visualize how CVs align with each other, and even make a prediction as to what someone’s next job will be.
Byron Galbraith is the Chief Data Scientist and Co-Founder of Talla, a startup leveraging the latest advancements in AI to build intelligent assistants for business teams. Byron has a PhD in Cognitive and Neural Systems from Boston University and an MS in Bioinformatics from Marquette University. His research expertise includes brain-computer interfaces, neuromorphic robotics, spiking neural networks, high-performance computing, and natural language processing. Byron has held several software engineering roles including back-end system engineer, full stack web developer, office automation consultant, and game engine developer at companies ranging in size from a two-person startup to a multi-national enterprise.