A search tool for recruiters that surfaces the most vital information on creative talent from around the web.
Recruiters have to sift through a plethora of platforms to gather talent portfolios and information. This requires an immense amount of time and effort in a space where speed equals success. I helped create a tool that feeds agents the most vital information for candidate decision making from across many sources (i.e. LinkedIn, Behance, etc.), eliminating the need for agents' arduous engagement on multiple platforms.
By conducting user research to arrive at sets of Image Classifier Labels to accompany Object Detection Label models, we were able to concoct a database of talent information which could be searched through with the aid of natural language processing. I collaborated with engineering to discover multiple multiple ways to execute, filter, and control search parameters. I also tested a variety of visualizations of search results, primarily to identify the best form factors to house the highest value ranked information to ensure an optimal experience for agents and their search habits.
We created a search tool that was vastly improved from Aquent's legacy search feature. In just 2 months it surpassed the legacy search platform in session count by 1.2x and increased 'redeployable fills' by 12%, leading to more recruiters placing more talent, faster.
More detailed information about the research, strategy, execution, and success of this project is available on request.