Online hiring platform.


The core value and feature of the product is search (for jobs etc.)

The client had 20GB+ historic data, and the goal of the project was to identify those users that:
(a) Might be interested in receiving the job posting as a push recommendation and (b) that are also appropriate candidates for the given job

Brainstorming over paper
Photo by Helloquence / Unsplash

What we did

A set of relevant modeling features was created: features that describe similarity (e.g., match user vs. job industry, career level, etc.; calculate the job title vs. user roles similarity, etc.), temporal features (e.g., capturing a user’s behavior activities in a recent time window, etc.). The dataset was labeled based on the user’s reaction, including cases when the user did not click on a recommended post. Classification + regression methods were used to distinguish and rank jobs to be recommended to users.


Client-reported increased search queries and a positive outcome. Search quality greatly influences customer perception of overall product quality.

And one more great thing about the project was that this is a fast feature - shipped within 2 months.