Greenhouse ATS Scorecards: How Your Resume Maps to 5 Recruiter Attributes

Greenhouse’s scorecard isn't a post-interview formality; it's the initial screening engine. Recruiters at companies like Notion, Dropbox, and Stripe configure five primary attributes, each with a custom weight (e.g., 'Technical Depth' weighted at 40%, 'Impact' at 30%). Your PDF is parsed into structured data tables, and the ATS performs a keyword density analysis against each attribute's underlying taxonomy. A ‘Technical Depth’ attribute at a cloud company prioritizes tokens like `AWS Lambda`, `Terraform`, `p99 latency`, and `K8s`. A generic bullet point about 'managing cloud infrastructure' returns a null value, scoring a 1.

This is not a simple keyword matcher. Greenhouse's algorithm assesses context. It looks for proximity of a technology term to a production metric. 'Built a Python service' scores low. 'Built a Python (FastAPI) service reducing API p99 latency from 2.1s to 185ms' maps tokens to both the 'Technical Depth' and 'Quantified Impact' attributes. The recruiter's UI displays these scores as a visual dashboard, creating an immediate, data-justified gut check long before human judgment is applied.

When you upload your resume, Greenhouse first converts it to XML, mapping sections to a rigid table structure. Your 'Experience' heading becomes a `<table>` with `<tr>` for each role. Parsing errors here—from fancy graphics, text boxes, or multi-column layouts—result in empty table cells. If your job titles or company names don't populate correctly, the system can't correlate your tenure with the required 'Years of Experience' attribute.

For each configured attribute, the system runs a scan. Let's say 'Problem-Solving Scope' is weighted at 20%. The ATS searches for phrases indicating system scale (`QPS`, `RPS`, `DAU`), complexity (`migration`, `refactor`, `legacy system`), and autonomy (`led`, `architected`, `initiated`). A bullet lacking these signals earns a low score. The recruiter sees a dashboard with sliders—not your original PDF. They might sort all candidates by the 'Impact' attribute score. A candidate with a 4/5 on Impact but a 2/5 on 'Technical Depth' might still advance if the role weights Impact higher, a decision pre-baked into the process before a human reads a word.

**Weak:** 'Responsible for improving database performance and team productivity.'

This fails. 'Responsible for' is a weak action verb. 'Improving' is vague. No technology tokens or metrics map to any attribute. The ATS scores this near zero for Technical Depth and Impact.

**Strong:** 'Designed and implemented a Redis caching layer for PostgreSQL, reducing average query latency by 74% and freeing up 20 engineering hours/week.'

This passes. 'Designed and implemented' signals autonomy. 'Redis,' 'PostgreSQL' map to Technical Depth. 'Reducing...by 74%' maps to Quantified Impact. '20 engineering hours/week' maps to Operational Efficiency, another common attribute. The ATS extracts clear tokens for multiple weighted scorecard buckets.

**Weak:** 'Led a team and collaborated with cross-functional partners.'

Pure fluff. 'Led a team' is too generic. No scale ('team of 4 engineers'), no business context. Scores low on 'Leadership & Influence.'

**Strong:** 'Led a 4-person eng team through a 6-month platform migration to GCP, achieving zero downtime and a 12% reduction in monthly cloud spend ($224k annualized).'

Specifics ('4-person,' '6-month,' 'GCP') provide scope. 'Zero downtime' and '12% reduction...$224k' are high-value impact metrics that directly feed the 'Impact' and 'Project Leadership' attribute calculations in Greenhouse.

Conventional wisdom says to lead with company prestige. Inside Google and Amazon hiring committees, we often discounted brand names if the scorecard lacked binary outcome verbs. 'Shipped a feature at Meta' tells us nothing. 'Shipped' is passive; the ATS needs verbs like 'Increased,' 'Reduced,' 'Migrated,' 'Cut,' 'Saved.'

The insider truth: Greenhouse scorecard attributes are designed to surface candidates who drive discrete, measurable change, not those who merely participated. A resume from a lesser-known company packed with phrases like 'Increased conversion by 11 percentage points' or 'Cut AWS costs by $1.2M' will consistently outscore a brand-name resume full of 'Contributed to' and 'Worked on.'

Recruiters trust the scorecard because it mitigates bias toward pedigrees. Your task is to engineer your bullet points as clear, machine-readable statements of cause ('Implemented X') and effect ('resulting in Y metric'), making the ATS's parsing job trivial and your attribute scores high.

**1. Audit for Attribute Keywords.** Scan each bullet. Does it contain a clear technology/tool (for Technical Depth) AND a hard metric with a unit (%, $, ms, hours) for Impact? If not, rewrite it.

**2. Structure for Parsing.** Use a single-column, standard section layout (Experience, Education, Skills). Avoid headers/footers with text. Ensure every job entry starts with Job Title, Company, Dates on one line—this is how Greenhouse's XML parser builds its primary table.

**3. Verb Enforcement.** Replace every 'Helped with,' 'Participated in,' 'Responsible for' with a binary outcome verb: 'Deployed,' 'Automated,' 'Reduced,' 'Scaled,' 'Launched.' These verbs trigger positive signals in the 'Initiative' and 'Impact' attribute logic.

**4. Metric Density Target.** Aim for at least one quantified, business-relevant metric per role, preferably two. For a 3-role resume, you should have 3-6 clear metrics. This is the single biggest lever for boosting your 'Quantified Impact' attribute score.

**5. Skills Section as Token Bank.** Your 'Skills' section is a direct keyword feed. List technologies (Python, React, Terraform), methodologies (Agile, CI/CD), and relevant domains (Fraud Detection, ML Ops). This section explicitly fuels the 'Technical Depth' attribute scan.