Moving students from overwhelming job search to intuitive career discovery

When looking for a job, students are generally navigating three distinct phases: discovery, browsing, and shortlisting. For Gen Z students entering a hyper-competitive market, the legacy model of endless scrolling through heavily padded, marketing-led listings creates massive cognitive overload. They don’t need a pretty interface that only shows two jobs at a time; they need a high-utility system built for volume.

To solve this, I shifted the product’s visual and structural language from an editorial, marketing-first experience to a high-density, system-led model. By looking at how digital-native students naturally explore and organise data, I architected a two-stage triage system heavily inspired by the cognitive patterns of email management. The goal was simple: separate high-level intentionality from deep research, giving students a powerful workspace to filter, favourite, and manage their recruitment cycles at scale.

For the tracker interface, I experimented with several different layouts, from standard cards to a hybrid card-table setup, but ultimately chose a high-density table view optimised for rapid decision making.

Under the hood, trackers are essentially just smart saved searches. Any time a student saves a search, it automatically creates a tracker, and we use their engagement data to suggest other trackers they might want to add to their list.

By stripping back the interface to showcase only the essential metadata, like organisation, role, location, and closing date, students can quickly scan their trackers and flag roles at scale. This was a deliberate choice of function over form. Rather than trapping newly tracked jobs in pretty cards where you can only see two at a time, the high-density table lets students process as many roles as possible in one glance.

This view is all about prioritising high-level intentionality over deep research, allowing students to quickly build up their shortlist of favourite jobs without getting bogged down in cognitive overload.

The core idea behind the tracker was to let users set notifications for whenever new or upcoming roles hit the platform. We also flag when a favourite role is closing soon, which is crucial because our data shows that the vast majority of students submit their applications within the final few days before the deadline.

Taking another cue from email psychology, we introduced a distinction between a job being brand new and a job simply being unread, or not interacted with. This stops students missing new opportunities if they log back in on a different day and assume they have already reviewed everything. While we do have a dedicated recent-activity feature, this unread state provides a much quicker, upfront indicator right where they need it.

Once a student flags a role, it transitions to the micro-execution phase within the jobs shortlist. Here, the interface shifts from rapid triage to focused execution, providing the necessary layout space for deep research and application tracking. Within this workspace, I implemented manual status-progression logic for stages like applied, interviewing, and offer, which successfully centralised the student’s entire search history in one reliable place.

In this view, the student can deep-dive into the details without losing their place. I structured the layout to pull in a comprehensive summary of the organisation’s profile alongside the full job description, allowing students to evaluate the opportunity and apply directly from this screen. By keeping everything in a single, focused workspace, we removed the need to constantly jump between tabs and browser windows during the application process.

By replacing the student’s manual spreadsheet with a high-density workspace, we didn’t just build a new UI, we changed the user’s mental model. This project proved that for a high-stakes audience, reducing cognitive noise and prioritising utility is the most effective way to drive long-term retention.

This architecture is currently preparing for launch. While live metrics are pending, our design hypothesis predicts a significant increase in both return rates and application volume. By replacing the student’s manual spreadsheet with a high-density workspace, we anticipate a fundamental shift in user behaviour: moving the platform from a marketing site they occasionally visit into an essential daily tool they rely on.

A couple of ideas we will look to add in phase 2:

• API into our internal ATS and other ATSs to automatically update the status of roles students are applying for.
• Skills matching that is visual to the user, based on their profile.
• Events in the platform using this exact same pattern; our data shows a high conversion rate of students attending an event and then applying for the role immediately after.