Wonderlic Select + Greenhouse integration: Automate pre-interview assessments, scoring, and candidate prioritization

Wonderlic Select integrates with Greenhouse to deliver pre-interview assessments that measure cognitive ability, personality, and motivation specific to each role. The integration automates candidate invites from Greenhouse, returns a single aggregated score and stack-ranking for easy comparison, and places assessment results directly on candidate profiles to support faster, evidence-based decision making. This article explains what the integration does, which hiring programs gain the most value, practical setup and workflow examples, measurable benefits, and recommended implementation steps so teams can decide whether and how to adopt Wonderlic Select in Greenhouse.

How the integration works in practice: when a candidate progresses to a configured stage in Greenhouse (for example, 'Phone Screen' or 'Assessment'), Greenhouse triggers Wonderlic Select to send an assessment invite. Candidates complete multi-measure assessments; results (single score, component breakdowns, and stack rankings) are pushed back to Greenhouse. Hiring teams then filter or sort candidates by score and schedule interviews for the highest-predicted performers. Because Wonderlic’s scoring is role-specific via an AI Job Profile, each job posting maps to assessment weighting that reflects the abilities and traits most predictive of success in that role. That makes the output actionable: teams can reliably prioritize interview bandwidth toward candidates most likely to perform and stay in the job.

Core features of the Wonderlic Select + Greenhouse integration

• Automated assessment invites Trigger candidate invites from configurable Greenhouse stages so assessments are sent without manual steps.
• Role-specific AI Job Profiles Built-in AI profiles generate the right blend of cognitive, personality, and motivation weights for each job title.
• Single aggregated score Complex scientific measures are combined into one score to simplify comparisons and reduce cognitive load on recruiters.
• Stack ranking of candidates Automatic ranking highlights relative strengths and weaknesses among your candidate pool for the same job.
• Results on candidate profile Assessment outputs are displayed inside Greenhouse, keeping workflows centralized and reducing context switching.
• No partner implementation fee According to available documentation, there is no partner implementation fee for the integration, which reduces initial friction for adoption.

Quick comparison: Wonderlic Select via Greenhouse vs. Manual resume screening

Who benefits most from this integration: high-volume hiring teams, centralized sourcing teams, and hiring managers who need an objective pre-interview filter. Typical use cases include hourly operations (retail, contact centers), transactional sales roles, and early-stage screening for professional roles where cognitive fit and motivation are correlated with success. Smaller companies and teams filling niche senior roles can also use Wonderlic Select, but the highest ROI is usually seen where screening time is a major bottleneck and where consistent, comparable signals across many candidates reduce subjective variance in shortlisting.

Recommended implementation steps (practical sequence)

• Define pilot job(s) Choose 1–3 roles with clear success metrics (e.g., retention at 90 days, performance rating) to validate assessments.
• Map job profiles in Wonderlic Use the AI Job Profile to set weights for cognitive, personality, and motivation dimensions relevant to each pilot role.
• Configure Greenhouse stages Set the stage(s) that should trigger assessment invites (application, prescreen, or later).
• Connect API and test Enable the Wonderlic integration in Greenhouse, complete a test invite, and confirm results return to the correct candidate profile.
• Run a controlled pilot Invite a subset of live candidates for the pilot period and track KPI baselines and changes.
• Calibrate cutoffs Adjust score thresholds or use stack rank percentiles based on pilot outcomes to balance pass rates and interview volume.
• Train hiring teams Provide short training on reading scores, interpreting component breakdowns, and aligning with behavioral interview plans.
• Measure and iterate Review time-to-hire, interview-to-offer, and retention after hires; refine job profiles and thresholds regularly.

Common implementation and compliance questions

Best practices for configuring AI Job Profiles and score thresholds: start with evidence from existing incumbents. If you have performance data (ratings, tenure), use it to validate which assessment dimensions correlate with success. For roles with limited historical data, rely on the AI Job Profile default but plan to iterate after an initial pilot of 100–200 candidates. When setting thresholds, consider using percentile cutoffs (for example, invite the top 30–40% by score for interviews) rather than absolute cutoffs at first—percentiles adapt to applicant pool quality and help manage interview capacity.

KPIs to track for evaluating impact

• Time-to-hire Measure whether screening time and total cycle time drop after integrating assessments.
• Interview-to-offer ratio A lower ratio indicates better pre-interview selection—track whether fewer interviews produce the same or more offers.
• Quality-of-hire Use early performance metrics (e.g., 90-day ratings) to validate predictive value.
• Retention / turnover Assess whether hires sourced using assessments exhibit improved retention over baseline.
• Candidate completion rate Monitor drop-off between assessment invites and completions to adjust timing and communications.

Sample Greenhouse workflow with Wonderlic Select

Candidate experience considerations: assessments should be concise and clearly communicated. Wonderlic Select focuses on multi-measure assessments but keep candidate time-to-complete in mind—shorter, targeted assessments typically yield higher completion rates. Provide clear instructions, estimated time-to-complete, and an explanation of why the assessment helps the hiring process to reduce drop-off. Also, ensure reasonable accommodations are documented and offered in compliance with employment laws; make retake policies and confidentiality details explicit in candidate communications.

Limitations and practical mitigations

• Not a replacement for interviews Assessments predict likelihood of success but should be combined with interviews and reference checks for a complete view.
• Potential candidate fatigue Keep assessments brief and avoid stacking multiple long tests in early stages; communicate expected time clearly.
• Legal and job-related validation Document job relevance and validation steps, especially for regulated roles; maintain audit trails for assessment use.
• Cultural and language considerations If hiring outside supported regions or languages, validate that assessments are appropriate or seek localized versions.

Interpreting scores and practical examples

Implementation checklist and timeline estimate: for a minimal pilot, plan for 2–6 weeks. Week 1: select pilot jobs and connect Wonderlic to Greenhouse. Week 2: configure job profiles and test end-to-end invites. Weeks 3–4: run pilot with live applicants and collect completion + qualitative feedback. Weeks 5–6: analyze KPIs (time-to-hire, interview-to-offer, completion rates), adjust thresholds, and scale to additional roles. Use a cross-functional team: recruiter (process owner), hiring manager (acceptance/thresholds), HR compliance (data/privacy), and an IT/admin to manage API and integration settings.