Reczee Screeno for Greenhouse: Automated Resume Screening for Technical Hiring

Reczee Screeno's Greenhouse integration automatically screens incoming applicants and adds contextual tags to candidate profiles within the Greenhouse pipeline. The integration is designed to move candidates from a large unordered applicant pool to a prioritized, filterable shortlist in under a minute—minimizing the time recruiters spend opening and reading individual resumes. This article explains how the integration works, who gains the most value from it, and the measurable benefits hiring teams can expect. It also includes setup steps, sample workflows, data sources Reczee Screeno uses, and practical best practices for combining automated tags with manual review.

How the integration works: Reczee Screeno connects to your Greenhouse account and evaluates applicants according to configurable rules and enrichment sources. It then appends standardized tags (for example: Top Company, Top Institute, Open Source Enthusiast, Hackathon Winner) and structured fields (graduation year, post-graduation year) to each candidate record so you can filter and sort in Greenhouse. The tool can be scoped: select which jobs, application sources, or pipeline stages should trigger automated tagging so only relevant applicants are processed. Tags are derived from both resume content and external achievement sources such as GitHub, LeetCode, and Codeforces to give a broader view of candidate capability beyond plain text.

Core features at a glance

• Fast applied-to-shortlisted workflow Automatically mark and tag applicants so teams can identify shortlist-worthy candidates in approximately 60 seconds after application.
• Automated, standardized tags Tags like Top Company or Hackathon Winner are applied consistently, removing manual tagging variability and enabling reliable filtering.
• External achievement enrichment Profile signals from GitHub, LeetCode, Codeforces and other public sources are combined with resume content to surface practical skills and activity.
• Structured experience attributes Graduation and post-graduation years are published to each candidate profile so teams get a stable proxy for experience even when resumes age.
• Configurable scope Choose which jobs, sources, and pipeline stages trigger tagging so the integration fits existing workflows without over-processing.
• Search and filter ready All tags and structured fields are searchable within Greenhouse, enabling rapid candidate segmentation and prioritization.

Data sources and what gets extracted

Who should consider this integration

• High-volume applicant teams Recruiting teams that receive hundreds to thousands of applicants per role and need to triage quickly.
• Technical hiring teams Engineering and product roles where GitHub and coding-platform activity are strong predictors of practical skill.
• Small talent teams without large sourcer headcount Teams that need to prioritize candidates efficiently without adding manual screening hours.
• Companies scaling campus or early-career hiring Graduation-year extraction and institute tags speed campus shortlists and help detect fresh graduates.
• Organizations already using Greenhouse Teams that want an inline enhancement to their existing ATS rather than a separate screening tool.

Screening beyond resume text is a major differentiator. Reczee Screeno correlates achievements and activity from multiple external platforms with resume entries—so a candidate who has few resume lines but an active GitHub with meaningful contributions will surface as an Open Source Enthusiast. This enriches the shortlist with behavioral and activity signals recruiters would otherwise miss. That said, enrichment is additive: it complements resume parsing rather than replacing recruiter judgment. The integration provides qualitative signals to prioritize which profiles to review manually first.

Filtering and tagging controls (practical examples)

• Stage-based tagging Tag only applicants in the Application Review stage for Software Engineer roles so only relevant candidates are processed.
• Source-based filtering Apply tags to candidates from job boards and referrals differently — for example, prioritize referrals for immediate review.
• Job-role scoping Limit tag application to specific job families (backend, frontend, data science) to reduce noise.
• Tag selection Choose which tags are applied and visible in Greenhouse; hide or disable tags that don't match your evaluation criteria.

Typical KPI improvements observed

Implementation checklist (practical steps)

• Authorize Greenhouse connection Grant Reczee Screeno access to candidate profiles so it can append tags and structured fields.
• Define tagging scope Choose jobs, sources, and pipeline stages that should trigger automatic tagging to match your workflow.
• Select tags and enrichment sources Decide which tags (Top Company, Open Source Enthusiast, etc.) and external sources to enable for enrichment.
• Pilot on a small set Run a pilot on 1–3 roles to validate tag accuracy and adjust filters before scaling.
• Train hiring teams Share tagging definitions and best practices with recruiters and hiring managers so tags are interpreted consistently.

Data privacy and compliance: Reczee Screeno publishes a privacy policy and follows the standard data access model used by Greenhouse plugins. Organizations should review the Reczee Screeno privacy policy and Greenhouse support documentation to confirm data retention, permitted uses, and any region-specific requirements. If you handle applicants in regulated industries or in regions with strict data laws, include your legal and privacy teams during setup to confirm alignment with company policies and local regulations.

Common questions recruiters ask

Limitations and best practices: automated tagging is only as useful as the configuration and data sources it uses. False positives can occur when resumes use ambiguous language or public profiles are sparse. To reduce noise, tune tag thresholds during the pilot and combine tags with simple rule-based filters (for example, require both Top Institute and relevant keywords for senior roles). Best practice: use Reczee Screeno to rank and segment candidates, then apply short manual reviews for the top segments. That hybrid approach yields speed without sacrificing nuanced human judgment.

Sample ROI calculation (per 100 applicants)