How your score is computed

What the number claims

With a posting it measures how well your resume lines up with what that posting asks for. Without one it measures your dates, achievements, sections, how your bullets are written, and whether the file can be read at all. It does not predict whether you will be interviewed, because that depends on the other applicants and the hiring bar on the day.

No language model is involved, so the same inputs give the same number. Recent experience is measured against today, so a resume you do not update drifts down over time.

The scored categories

These categories carry 85% of the final score. A formatting check carries the other 15%. The weights below are what each category is worth when every one can be scored, and they vary by role type.

The first five compare your resume against a posting and switch off when you paste none. The last two read your resume on its own. A switched-off category spreads its weight across the rest, so the applied weights can differ from the table below. The worked example shows both.

You need two scored categories for a number, and recent experience and section completeness cannot be the only two, because they measure the shape of the document rather than what it says. Below that the checker says what to add instead of showing a score.

Minimum scores

A category you have not addressed at all lands on a minimum rather than a zero.

• Keyword match. 55 when the posting yields no hard requirements and your resume shows related experience.
• Title and seniority alignment. 38 when your titles share no words with the target title. Sharing one word is worth at least 74.
• Recent experience. 55 when no date on the resume can be read.
• Achievement quality. 45 when you have bullet points and none of them carry a number.
• Education and certifications. When the posting asks for nothing: 55, or 80 with a degree. When it asks: 90 with the qualification, 35 without.
• Section completeness. 40 when none of the four sections can be found, rising 15 for each one that is there.
• Bullet quality. 40 when every bullet opens on a weak phrase, 100 when none of them do.

What the bands mean

• Excellent80 to 100
• Strong65 to 79
• Competitive50 to 64
• Needs Optimization0 to 49

A worked example

This is what the checker returns for the resume and posting below.

Priya Raman
Berlin, Germany | priya.raman@example.com | +49 30 1234567
linkedin.com/in/example

PROFESSIONAL SUMMARY
Backend engineer with six years building payment and billing services for
high-volume marketplaces.

EXPERIENCE

Senior Backend Engineer, Northwind Payments
March 2022 - Present
- Rebuilt the settlement pipeline in Go, cutting end-of-day reconciliation from
  four hours to eleven minutes.
- Introduced idempotency keys across the refunds API, eliminating a class of
  double-refund incidents that had cost roughly 40,000 EUR a year.
- Led the migration of 38 services from self-managed Kubernetes to EKS.

Backend Engineer, Halberd Retail
June 2019 - February 2022
- Built the inventory reservation service in Python and PostgreSQL, serving
  1,200 requests per second at peak.
- Added Kafka-based event replay, reducing failed-order recovery time by 70%.

EDUCATION
BSc Computer Science, Technical University of Munich, 2019

SKILLS
Go, Python, PostgreSQL, Kafka, Kubernetes, Docker, Terraform, AWS, gRPC, Redis

Senior Backend Engineer

We are building the payments platform that European marketplaces run on.

Required Skills
- Go, Java, PostgreSQL, Kafka, Kubernetes, Terraform
- gRPC, Redis, Datadog, OpenTelemetry
- PCI DSS, double-entry accounting, ledger design

Preferred Qualifications
- Elixir, Rust, GraphQL

Responsibilities
- Own the settlement and refunds services end to end
- Partner with compliance on regulatory reporting

Where the score is weakest

• Prose postings. A posting written as flowing paragraphs with no requirements list often yields no keywords at all. Paste its requirements section on its own and the keyword match works.
• Literal matching. A posting asking for Kafka does not match a resume saying Kinesis. Your keyword score reads lower than a human reviewer would score the same experience.
• Layout. Your file arrives here as text, not geometry. A two-column layout, a table or a rating graphic is already flattened by the time it is scored, so nothing here tells you whether one survives a particular ATS.
• No outcome data. These scores have never been measured against interview rates. A higher number is not evidence of more interviews.