PayScope's timeline:
May: Launched our public beta, achieving #8 on Product Hunt among 150+ products. Based on initial user feedback, we immediately shipped a mobile-adaptive version.
June: First 1K salary calculations. User feedback drove the decision to launch our full marketing website for SEO and initiate a complete product redesign.
July: First visitors from organic search. We shipped v.1 of the web app, featuring the new design, deeper resume analytics, and the initial version of the personal account. First registered users!
Hey Product Hunters!
I’m Anton, the data guy behind PayScope and the talking head you saw in the demo video above. I’d love to tell you a bit about how our product works under the hood.
PayScope is backed by our AI parsing and pricing models. It is trained on 27 million vacancies so far, and we collect millions of vacancies each month to improve pricing. Both models are concerned with attributes of resume and job descriptions, such as skills, type of employments, type of employer, industry, education level, location, certifications and qualifications. These attributes get converted further into numerical vectors, placing candidates and job openings in the same space. Each job vacancy includes a known salary, which we use as the target value. Using these data points, we train a model to understand the relationship between job characteristics and compensation. When a new candidate’s profile is parsed and embedded, it’s run through the model to estimate a fair salary based on how well they align with current job openings.
I’d love to hear from you. Thanks for checking us out!