Pymetrics + Lever
Connect Pymetrics and Lever for a Faster, Fairer Hiring Pipeline
Push neuroscience-based candidate assessments straight into Lever so your team can move faster without cutting corners on quality or fairness.
Why integrate Pymetrics and Lever?
Pymetrics measures candidate potential through neuroscience games and AI-driven assessments. Lever tracks and manages the full recruiting lifecycle. Used together, they should be a powerful combination — but only if the data actually flows between them. With tray.ai, assessment scores, trait data, and fit indicators automatically land in Lever candidate profiles the moment a candidate finishes their evaluation. No manual exports, no copy-pasting, no stale records. Recruiting teams get the full picture on every candidate, which means faster decisions and fewer gut-feel guesses.
Automate & integrate Pymetrics & Lever
Use case
Automatic Candidate Profile Enrichment
When a candidate completes a Pymetrics assessment, their trait scores, cognitive attributes, and fit indicators are automatically pushed into their Lever profile as structured notes or custom fields. Recruiters don't need to jump between platforms or copy anything by hand. Every Lever profile stays current from the moment assessment results come in.
Use case
Assessment Invitation Triggered by Lever Stage Progression
When a recruiter moves a candidate to a specific stage in Lever — like 'Phone Screen Passed' or 'Assessment Requested' — tray.ai automatically sends that candidate a Pymetrics assessment invitation. Candidates get what they need at the right moment in the process, and your recruiting team doesn't have to remember to do it manually.
Use case
Automated Stage Advancement Based on Assessment Results
When a candidate's Pymetrics results come in above a predefined fit threshold for a role, tray.ai can automatically advance them to the next Lever stage and notify the recruiter or hiring manager. Top candidates stop sitting in limbo waiting for someone to notice their results.
Use case
Candidate Rejection Workflow for Low-Fit Assessments
When a candidate's Pymetrics results fall below the fit threshold for a role, tray.ai can automatically update their Lever stage and trigger a personalized rejection message. Candidates get respectful, timely feedback, and your recruiting coordinators aren't buried in rejection emails.
Use case
New Lever Applicant Sync to Pymetrics
When a new candidate applies through Lever and hits a qualifying stage, their contact details and role information are automatically synced to Pymetrics, registering them for the right evaluation before anyone has to lift a finger. Both platforms stay in sync from the very first step of the candidate journey.
Use case
Recruiter Notifications for Completed Assessments
When a candidate finishes their Pymetrics assessment, tray.ai immediately notifies the responsible recruiter and hiring manager via Slack, email, or directly in Lever as a task or note. No one has to manually check Pymetrics to find out what's happened.
Use case
Diversity and Inclusion Reporting Pipeline
By combining Pymetrics trait data with Lever's pipeline data through tray.ai, talent acquisition leaders can build automated reports tracking how candidates from diverse backgrounds move through each hiring stage relative to their assessment results. When bias is hiding in your funnel, this is how you find it.
Get started with Pymetrics & Lever integration today
Pymetrics & Lever Challenges
What challenges are there when working with Pymetrics & Lever and how will using Tray.ai help?
Challenge
Matching Candidates Across Two Separate Systems
Pymetrics and Lever each keep their own candidate identity records. Without a shared unique identifier, matching the same person across both platforms means relying on email address or name — which breaks down quickly when candidates use different emails at different stages.
How Tray.ai Can Help:
tray.ai's data transformation tools let teams build candidate matching logic that normalizes email addresses, handles alternate identifiers, and flags ambiguous matches for manual review, so data stays clean and reliable between the two platforms without duplicate records piling up.
Challenge
Keeping Role-Based Assessment Profiles in Sync
When hiring teams update job requirements or role profiles in Lever, the matching Pymetrics assessment configuration needs to change too. Without an automated sync, these two drift apart and candidates end up evaluated against outdated benchmarks — which undermines the whole point of using objective assessments.
How Tray.ai Can Help:
tray.ai can watch Lever for job posting updates and trigger notifications or automated updates in Pymetrics to prompt assessment profile reviews, so role definitions and evaluation criteria stay consistent across both platforms.
Challenge
Handling Webhook Failures and Assessment Data Delays
Real-time integrations between Pymetrics and Lever depend on reliable webhook delivery. Network issues, platform outages, or API rate limits can cause assessment results to arrive late or out of order, leaving Lever profiles temporarily incomplete and recruiters working from stale data.
How Tray.ai Can Help:
tray.ai has built-in error handling, retry logic, and workflow monitoring that automatically re-attempts failed webhook deliveries and alerts operations teams to any sync failures, so assessment data reaches Lever reliably even when things go wrong.
Challenge
Managing Multi-Role Candidate Assessments
Candidates applying to multiple roles at once may need separate Pymetrics assessments for each position. Their results have to land in the right Lever opportunity, not get merged into a single profile — something manual processes handle badly and inconsistently.
How Tray.ai Can Help:
tray.ai's workflow logic handles multi-opportunity candidate scenarios by mapping Pymetrics results to specific Lever opportunity IDs rather than just candidate profiles, so multi-role applicants get accurate, role-specific assessment attribution throughout.
Challenge
Compliance and Data Retention Across Platforms
Candidate assessment data from Pymetrics carries real legal weight, particularly under GDPR, CCPA, and EEOC guidelines. Making sure sensitive neuroscience results are handled, stored, and deleted according to retention policies across both platforms is an operational headache most recruiting teams aren't set up to manage manually.
How Tray.ai Can Help:
tray.ai lets teams build automated data governance workflows that enforce retention schedules, trigger deletion requests across both platforms at once, and maintain audit logs of all data movements between Pymetrics and Lever, so compliance doesn't fall to your recruiting ops team to track by hand.
Start using our pre-built Pymetrics & Lever templates today
Start from scratch or use one of our pre-built Pymetrics & Lever templates to quickly solve your most common use cases.
Pymetrics & Lever Templates
Find pre-built Pymetrics & Lever solutions for common use cases
Template
Pymetrics Assessment Completed → Update Lever Candidate Profile
This template listens for a completed assessment event in Pymetrics and automatically writes the candidate's trait scores, fit rating, and assessment metadata into the matching Lever candidate profile as structured notes or custom field values.
Steps:
- Trigger: Pymetrics webhook fires when a candidate completes their neuroscience assessment
- Lookup: Match the candidate in Lever by email address or unique candidate ID
- Action: Write Pymetrics trait scores, fit rating, and completion timestamp to the Lever candidate record as notes or custom fields
Connectors Used: Pymetrics, Lever
Template
Lever Stage Change → Send Pymetrics Assessment Invitation
This template watches Lever for candidate stage changes and automatically sends a Pymetrics assessment invitation when a candidate hits a designated stage like 'Assessment Required,' so evaluations go out on time and consistently across every open role.
Steps:
- Trigger: Lever webhook fires when a candidate's pipeline stage is updated
- Filter: Check if the new stage matches the configured assessment trigger stage (e.g., 'Assessment Required')
- Action: Create a Pymetrics assessment invitation for the candidate with the appropriate role-based evaluation profile
Connectors Used: Lever, Pymetrics
Template
Pymetrics High-Fit Result → Advance Lever Stage and Notify Recruiter
When Pymetrics returns a result that meets or exceeds the configured fit threshold, this template automatically advances the candidate to the next Lever stage and sends the assigned recruiter a real-time notification with a summary of the assessment findings.
Steps:
- Trigger: Pymetrics webhook fires with completed assessment result and fit score
- Condition: Evaluate whether the fit score meets the role-specific threshold for advancement
- Action: Update the candidate's stage in Lever and send a Slack or email notification to the responsible recruiter with assessment highlights
Connectors Used: Pymetrics, Lever
Template
New Lever Applicant → Register Candidate in Pymetrics
This template watches Lever for newly created candidate records and automatically registers them in Pymetrics with the right role and evaluation profile, so every new applicant is queued for assessment without anyone having to do it by hand.
Steps:
- Trigger: Lever webhook fires when a new candidate application is created and reaches a qualifying stage
- Transform: Map Lever candidate fields (name, email, role) to the Pymetrics candidate registration schema
- Action: Create a new candidate record in Pymetrics and assign the appropriate role-based assessment profile
Connectors Used: Lever, Pymetrics
Template
Pymetrics Low-Fit Result → Archive Lever Candidate and Send Rejection
This template handles low-fit assessment outcomes by automatically archiving the candidate in Lever with a standardized reason, then optionally triggering a personalized rejection email so candidates hear back promptly and professionally.
Steps:
- Trigger: Pymetrics webhook fires with a completed assessment result below the configured fit threshold
- Action: Archive the candidate in Lever with a designated rejection reason tied to assessment outcome
- Action: Trigger a templated rejection email via Lever or a connected email service to notify the candidate
Connectors Used: Pymetrics, Lever
Template
Weekly Pymetrics-Lever Pipeline Summary Report
This scheduled template runs weekly to pull assessment completion rates from Pymetrics and pipeline conversion data from Lever, then combines them into a unified report delivered to talent acquisition leadership via email or a connected BI tool.
Steps:
- Schedule: Workflow triggers every Monday morning on a time-based schedule
- Fetch: Pull completed assessments and fit score distributions from Pymetrics for the prior week
- Fetch and Combine: Pull stage conversion metrics from Lever and merge with Pymetrics data into a formatted summary report delivered via email or pushed to a Google Sheet or BI dashboard
Connectors Used: Pymetrics, Lever