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  • Writer's pictureNathan Ross - Acquired by LinkedIn

Updated: Sep 10, 2019

First full-time Designer / Lead Designer

A job portal which used ML to connect job seekers with the best jobs. I led UX and UI design as the first full-time designer.

The problem

When I was brought on to Bright, they already had a working and funding MVP. At that type, Bright was focused on using job-seekers social networks as the main source of connecting people with the jobs they were applying for. This was definitely a long-term goal, but users were not entirely comfortable at that time in sharing job searches with their social network. So there was a need to focus on expanding what Bright represented, and how to scale the product at the same time.


My role and process

As Lead Designer at Bright, I worked on a full redesign from the initial MVP, along with A/B testing multiple user flow designs to help the site grow to over 2 million users. I managed a small team of graphic designers and front-end devs, and worked directly with the Product Manager in implementing analytic and user feedback into to the product redesign.

Team size: 3 Designers, 2 Front-End Engineers, 2 Data Scientist, 1 User Researcher, 1 Product Manager, multiple Back-end Engineers.

Research and testing methods: rapid-prototyping, A/B testing, analytics, user interviews.


The solution

With the flexibility of the dev team at Bright, we were able to test different product stories and see what worked with users. Also, with a strong data science team, we were able to explore specific data findings. One of these insights was that that job seekers were applying for far too many jobs (due to lack of clarity in job postings), and job posters were receiving far too many job applicants. With machine-learning, we were able to build in a feature known as a Bright-Score that showed both sides the best applicants and roles.

Job search view

Job listing view

Application view


As shown below, the Bright Score allowed users to to get recommendations for the job that matched based their background. This was done through building ML models from user's LinkedIn accounts, resumes, and Bright profile data.


This work also enabled the hiring side of the solution.

Hiring portal


One other tool that help grow Bright was Bright Labs, a blog which used data findings from the data-science team to help job-seekers get more insight into the best time to apply for jobs, what fields were popular, and general fun insights.

Bight Labs blog

The impact

Over the period I was at Bright, monthly users grew from 800k to 2.5MM through testing different marketing flows and product updates which I either designed, art-directed, or brainstormed with in some way. This time also included a full redesign of the tool, with launching of Bright Score and Bright Insights. These features allowed for continued growth which led to Bright being purchased by LinkedIn.



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