# Pymetrics + Lever integration > Push neuroscience-based candidate assessments straight into Lever so your team can move faster without cutting corners on quality or fairness. **Canonical page:** https://tray.ai/connectors/pymetrics-lever-integrations/ **Lever connector:** https://tray.ai/connectors/lever-integrations/ **Lever documentation:** https://tray.ai/documentation/connectors/service/lever ## Overview 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. When Pymetrics and Lever run as separate tools, someone on your team is stuck manually exporting assessment results and copying them into Lever. It's slow, error-prone, and creates the kind of data lag that lets good candidates fall through the cracks. Integrating the two through tray.ai means assessment data moves automatically from Pymetrics to Lever the moment a candidate finishes their evaluation. Recruiters can see cognitive and emotional trait scores right alongside resume and interview notes, which makes for better conversations and more defensible decisions. You can also standardize scoring thresholds across roles, trigger stage progressions in Lever based on Pymetrics results, and feed a reporting pipeline that actually gets better over time. ## Use cases ### 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. - Eliminates manual data entry between Pymetrics and Lever - Recruiters always see assessment data alongside resume and interview notes - Reduces the risk of data loss or transcription errors across candidate records ### 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. - Standardizes when assessments go out across all roles and hiring managers - Reduces candidate drop-off by delivering assessments promptly - Frees recruiters from manually initiating assessment requests ### 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. - Speeds up time-to-hire for high-fit candidates - No qualified candidate gets stuck waiting in a stale pipeline stage - Creates consistent, rules-based advancement criteria across all open roles ### 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. - Faster candidate feedback improves how people experience your hiring process - Reduces recruiter workload on rejection communications - Keeps rejection criteria consistent and grounded in objective data ### 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. - Eliminates duplicate candidate data entry across both platforms - No applicant gets missed or forgotten in the assessment queue - Cuts down time-to-assessment for new applicants ### 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. - Hiring teams hear about candidate activity right away - Faster response times mean a better candidate experience - Eliminates the need to manually poll Pymetrics for new completions ### 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. - Supports DEI work with automated, cross-platform data aggregation - Gives talent leaders concrete pipeline fairness metrics to act on - Reduces manual reporting effort for compliance and internal audits ## Templates ### 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. Connectors used: Pymetrics, Lever ### 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. Connectors used: Lever, Pymetrics ### 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. Connectors used: Pymetrics, Lever ### 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. Connectors used: Lever, Pymetrics ### 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. Connectors used: Pymetrics, Lever ### 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. Connectors used: Pymetrics, Lever ## Challenges Tray.ai solves ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/