Most recruiters treat a new job opening as a fresh start. A search goes out, applications come in, and the process rarely loops back to check who almost got a similar role six months or a year earlier. This habit wastes budget and productive time. However, Lever's tools for addressing this issue require recruiters to actively look into the database.
In this guide, we cover what Lever does with a company's stored candidate history once a new requisition opens, where that native falls short of full rediscovery, and how connecting an external AI tool closes the gap with an automated, scoped, and auditable search.
What Is Talent Rediscovery?
Talent rediscovery means searching and matching candidates already stored in Lever's ATS against a role that just opened. It covers applicants from closed jobs, candidates parked in a talent network, and anyone who advanced partway through a hiring process before the role got filled or shelved.
A year or two of application history turns into an active sourcing channel once those stored candidates get checked against every new opening automatically. Most ATS platforms leave that to a recruiter's memory. Rediscovery performs the check every time, without anyone needing to recall a name.
Does Lever Provide Candidate Rediscovery?
Partially, Lever's Nurture Recommendations feature ranks past applicants by quality for a specific job posting, using a weighted algorithm based on feedback scores, notes, archive reasons, and previous job postings.
A recruiter can also add similar job postings to widen the pool of past applicants considered. Talent Fit, a separate feature, flags top-matched candidates within a single job's own applicant pool, based on that job's requirements, with a stated justification for each match.
Neither tool triggers automatically. A recruiter has to open Recommendations and select a job posting each time, and recommended candidates are not tracked separately from other contacts in a Nurture campaign by default. Nothing scopes a search by how long ago a candidate applied, and nothing records a hiring manager's decision on a recommended name.
How to Rediscover Candidates in Lever
Lever's Nurture Recommendations already ranks past applicants by quality for a specific job posting. Connecting Skima AI automates the trigger, scopes the search by candidate date, and creates the hiring manager decision trail Nurture Recommendations does not track.
Below is a 5-step workflow that starts with what Lever already provides and builds on it:
Step 1: The Native Path, Start With Nurture Recommendations
Navigate to the Candidates section in Lever and click on Recommendations in the upper-right corner. Select a job posting from the dropdown menu. Lever's algorithm then ranks past applicants by quality, using feedback scores, notes, archive reasons, and previous job postings. A recruiter can add similar job postings to expand the candidate pool.
This step provides a ranked list based on the company's own applicant history, eliminating the need for a manual keyword search.
However, a recruiter must select a job posting each time. Recommended candidates are not tracked separately from other Nurture campaign contacts unless someone manually duplicates the campaign. For an automated, scoped, and tracked version of this same search, Skima AI builds on this next.
Step 2: Connect Your Lever Account With Skima AI
Create API credentials inside Lever, under Settings, then Integrations and API, then API Credentials. This connection uses Lever's own REST API directly, with no middleware required.
- Read access: opportunity data (contact, stage, source, owner, tags), contact data (name, email, phone, location, LinkedIn), resume files and application responses, posting details, and past opportunity history for rediscovery context
- Write access: activity notes and candidate tags, since Lever does not support custom fields on opportunities
- Event triggers: opportunity created or updated, stage changed, and file attached, delivered through Lever's webhooks
Step 3: Configure Tags and Enable Webhooks
Set up the Skima Screened tag inside Lever and confirm permissions for automated tagging and activity notes, since match score, reasons, and shortlist Link all post as activity notes rather than custom fields on the opportunity record.
Add the Skima AI webhook URL and select the event types it should react to. If webhooks are not configured, Skima AI falls back to a 10 to 15 minute polling interval instead.
Before rolling this out further, test the connection on two or three requisitions. Upload a resume and confirm the Match Score and Reasons appear as activity notes, then submit sample hiring manager feedback and check that it logs correctly.
Step 4: Launch Talent Rediscovery Against a Live Requisition
Open the job inside Skima AI, from the Jobs list or from within the job itself. Click the three-dot Actions menu and select Rediscover Candidates. A modal opens with options to narrow the scan: select a specific candidate segment, or leave it open to every synced Lever record.
Set the candidate created date range next: All time, Last 6 months, Last 1 year, or a custom window, and optionally check the location filter to restrict the scan geographically. Click Apply. A "Performing Rediscovery" notification appears while the scan works in the background, and turns green with a refresh option once it completes.
Step 5: Review, Filter, and Sync Feedback Back to Lever
Refresh the page and open the Rediscovered source tab inside View Candidates. Each match carries a Skima AI score and reason bullets, tagged with a Rediscovery source label. Filter by Industry, Work Mode, Experience Level, Notice Period, Location, and Salary Range to narrow the list.
Once the shortlist looks right, generate a secure, no-login link and send it to the hiring manager. They click thumbs up, thumbs down, or maybe on each name, with an optional comment. Skima AI writes that decision back into Lever as an activity note on the opportunity record, keeping the feedback on the record without a second login.
What If Your Lever Database Is Messy or Duplicated
Lever flags a possible duplicate profile when it finds a matching email address or when a full name closely matches another profile, including common name variations. A banner appears directly on the candidate profile in these cases. Recruiters can also bulk-select and merge profiles from the main candidate list or a search results list instead of resolving them one at a time.
That detection does not check phone numbers or LinkedIn URLs. Two profiles that share a phone number or LinkedIn profile but have different names and email addresses will not be flagged.
Block Repeat Applications, Lever's separate tool for preventing a second submission to the same job posting, only addresses that specific scenario. It does not catch a candidate applying for a different open role, and applications submitted through Lever's API bypass it entirely.
Skima AI detects phone numbers and LinkedIn URLs as matching signals in addition to email and name. It first matches on email, phone, or LinkedIn URL, then checks name, location, and company together as a backup signal.
When a match is found, a field comparison modal displays differences across Name, Title, Company, Location, Email, Phone, and LinkedIn, tagging each mismatched field as Differs. A recruiter chooses which values to keep and merges the records, or adds the profile as new if the two genuinely differ. Every resolved case is logged under the Duplicates tab, divided into In Database, From Uploads, and Resolved sub-tabs for later audit.
5 Benefits of Candidate Rediscovery with Skima AI in Lever
A documented 30-day pilot with Lever reported 5 clear advantages once Skima AI starts scoring a company's stored candidate history:
- Faster Time-to-Screen: Rediscovered candidates arrive already matched and scored against the open role, reducing time-to-screen by 58% compared to starting a fresh search.
- Interviews Double From the Shortlist: The same pilot saw interviews booked from the shortlist double compared to the prior process.
- A Score Attached to Every Match: Every rediscovered candidate carries a 0 to 100 score with reason bullets, replacing Nurture Recommendations' ranked-but-unscored list with a number a recruiter can compare across candidates.
- One Activity Timeline, No New Login: Match scores, reasons, and tags post as activity notes directly on the same opportunity record a recruiter already checks inside Lever.
- Manager Feedback That Sticks: Hiring manager satisfaction rose 24 points on NPS in the same pilot, tied to a feedback link that logs every decision instead of a verbal note.
5 Best Practices to Rediscover Talent in Lever
Getting significant ROI out of this connection takes a few habits beyond the integration itself. The 5 practices below cover search timing, data hygiene, and fitting rediscovery around Lever's opportunity-based structure:
- Start With Nurture Recommendations: Use the native ranking to catch obvious matches for a specific job posting first. Move to a scoped Skima AI scan when the role needs a numeric score, a date-limited pool, or a tracked hiring manager decision.
- Automate the Scan on Job Creation: Skima AI's setting under Preferences and General starts a database scan the instant a new job gets created inside the connected account, rather than waiting for a recruiter to select a job posting.
- Extend Reverse Search Beyond One Opportunity: A candidate with one closed opportunity in Lever can still fit a different open role well. Reverse Search starts from that candidate's profile and returns every open job they match, ranked by score.
- Segment Scans by Candidate Created Date: Choose a 6-month or 1-year window when a role calls for someone recently active. Widen to all time for a niche or hard-to-fill role where the applicant pool has stayed thin for years.
- Review the Duplicates Tab for Phone and LinkedIn Matches: Since Lever's own duplicate check only looks at email and name, checking the In Database sub-tab on a set schedule catches profiles that share a phone number or LinkedIn URL but nothing else.
Is Candidate Rediscovery Worth It for Your Recruitment Team?
Lever's Nurture Recommendations provides a ranked list of past applicants for a specific job, offering a preview of what its history can yield. The extent of this preview relies on the number of duplicate profiles linked to different phone numbers or LinkedIn URLs, as Lever's native check does not identify them.
Companies with years of closed opportunities across various job postings give Skima AI more history to score and deduplicate. In contrast, newer accounts with a limited opportunity history result in a thinner Rediscovered list, complicating early justification of the setup.
Pilot Candidate Rediscovery in Lever Now
For a company with a multi-year Lever history, a pilot with Skima AI can deliver a high ROI. Start with two or three live requisitions. Ideally, these should be roles the company has filled multiple times before, so there is a solid historical pool to score against.
Connect Skima AI to Lever and set up the Skima Screened tag. Test the connection on those two or three requisitions before going live. Skima AI's integration with Lever organizes the rollout this way: test on a small sample first, then go live on three pilot roles. Track progress through weekly ROI emails and checkpoints at day 15 and day 30.
Monitor how many rediscovered candidates reach an interview and how the hiring manager responds to the shortlist link. Skima AI's documented 30-day pilot with Lever showed a 58% drop in time-to-screen, a doubling of interviews from the shortlist, and a 24-point increase in hiring manager satisfaction on NPS. From there, expanding to more requisitions becomes a decision supported by the pilot's own numbers.
Frequently Asked Questions
1. Does Lever have a built-in talent rediscovery feature?
Partially, Lever's Nurture Recommendations ranks past applicants by quality for a specific job posting, but a recruiter has to select that posting manually each time, and nothing tracks a hiring manager's decision on a recommended name.
2. How do I rediscover candidates in Lever?
Start with Lever's native Nurture Recommendations for a ranked list tied to one job posting. For an automated, scoped, and auditable version, connect Skima AI through Lever's API, set up the Skima Screened tag, then launch Talent Rediscovery on a live requisition.
3. What are the benefits of candidate rediscovery in Lever?
Based on a documented 30-day pilot, rediscovered candidates reduced time-to-screen by 58% and doubled interviews from the top-10 shortlist. Matches carry an explainable score and reason bullets, and hiring manager satisfaction rose 24 points on NPS.
4. Is Skima AI safe to integrate with Lever for talent rediscovery?
Yes, Skima AI scores candidates using resume-based evidence only, with no automatic rejections, and logs every hiring manager decision and shortlist action for audit. Data stays encrypted in transit and at rest, backed by a signed Data Processing Agreement.
5. Is candidate rediscovery useful for Lever's typical customers?
Yes, especially for companies that source a large share of candidates directly and build up years of opportunities across many job postings. The value grows with the size of that closed-opportunity history and how often similar roles reopen.
