Business Strategy | Project Management | User Research | Team Building | Product Design | Data Science

Through machine learning and a focused user experience, the goal of Career & Company was to remove job search inefficiencies — finding the right match between people, jobs, and companies, quicker and more effectively.

Above: A video summary of the path we blazed as a result of my inspirational problem.
Click here if the video player controls are not functioning.
Career & Company Secondary Research
Above: Secondary market research for Career & Company.
Above: We were placing ourselves in fertile ground. While not immediately crowded, there were many signs that the big players would be moving to fill the void soon.
Meeting with Dev Team
Above: My guilty snack pleasures include Twizzlers, Pringles, and gummy bears (not pictured).
Above: A screenshot of an early prototype. By building a simplified version of the planned consumer-facing experience, we could gather feedback on the big ideas and basic functionality without developing the product on the back end. One aspect that could not be tested, however, was the actual job recommendation process (more on that later).
Career & Company Persona
Above: One of the personas on our office walls.
Above: A short audio excerpt from a card sort exercise facilitated at a Starbucks.
Above: While mockups like this were great at walking people through the concepts and getting abstract feedback, we weren’t able to gether any data about how successful our product could actually be with users. Until we created the email prototype, that is.
C&C Prototype
Above: A section from one of the forms we sent to our study participants.
Above: As we tested the prototype, we updated the algrorithm. It was too obvious of a problem to wait to address. Algo 3 was a bit of a dead end, but otherwise we were able to quickly realize significant gains.
Note: Due to possible confounds (as well as the small N), the results need to be taken with a large grain of salt. Nonetheless, the overall trend was clear and strong, and it was enough to inform future decisions in a fast, lean environment with tight deadlines.