JD Optimization: 4,000+ Job Descriptions, A Team of 10 or Less, The Fix

In this week’s episode of The JD Fix, I shared how one healthcare organization tackled a challenge that would make most TA teams sweat:

• A library of more than 4,000 job descriptions

• A team of fewer than 10 people

• And the pressure to keep recruiting operations moving while cleaning up the entire JD library

Sound familiar?

This team had thousands of live job descriptions across their system. The issues ranged from small annoyances to major blockers:

• Inconsistent formatting (some with headers, some without)

• Missing sections that left candidates guessing

• Subtle bias baked into language choices

• Hours of manual work whenever a recruiter needed to update or create a JD

And on top of that, they were working in healthcare—a high-volume, high-pressure space where candidate experience really matters.

Here’s the twist: they didn’t overcomplicate it.

Instead of creating dozens of department-specific templates, they went with just two:

1. A global template

2. A special projects template

With these, Ongig’s AI-assisted templating engine helped them roll out standardized, candidate-friendly job descriptions. Every posting had:

• Clear sections

• Readable headers

• Applicant-first language

Before Ongig, optimizing a single job description could take 30 minutes—or longer with multiple review cycles.

After Ongig? Under 7 minutes.

That’s hundreds of hours saved.

And the numbers prove the quality jump:

• Average JD score climbed from 70 → 90+

• Gender bias scores improved from 64 → 98.3

• 20% of jobs with “unmatched content” were automatically flagged, so the team only had to review the ones that mattered

This wasn’t just a one-time cleanup.

The team built a process that stuck. Anytime a recruiter (or even a hiring manager) tried to customize a posting in Taleo, Ongig automatically kept formatting and sections consistent.

Now, they’re exploring:

• Full automation of their JD workflows

• Deeper Taleo syncing

• AI-generated net-new JDs built directly from templates

And they’re doing it all with fewer than 10 people.

You don’t need a massive TA team to fix your JD library. You need:

• The right tools

• A simple process

• The ability to focus where it matters

This healthcare org proved it. They saved time, improved inclusivity, boosted readability, and set themselves up for long-term success.

If you’re staring down your own mountain of messy job descriptions, take a page out of their playbook.

👉 Want to see how Ongig can help your team do the same? Request a demo