Fit Score & Ranking

SmoothHiring uses a multi-dimensional scoring system to evaluate and rank candidates objectively. Understanding how scores are calculated helps you make faster, data-driven hiring decisions. This guide explains every scoring component, how rankings work, and how to use scores effectively in your workflow.

The Scoring Components

Each candidate receives multiple scores that together paint a complete picture:

Resume Match Score (0–100%)

How It's Calculated

The Resume Match Score uses AI to compare the content of a candidate's resume against the job description and requirements. The algorithm evaluates:

1. Skills Match – Do the candidate's listed skills align with required and preferred skills?
2. Experience Relevance – Are their past roles and responsibilities relevant to this position?
3. Education Alignment – Does their educational background meet the job's requirements?
4. Keyword Density – How frequently do job-relevant terms appear in the resume?
5. Seniority Alignment – Does their career level match what the role demands?

Score Ranges

Visual Display

The Resume Score appears as a circular gauge (donut chart) in the applicant list. The gauge fills proportionally and uses color coding:

• Green (high scores) – 80%+
• Amber (moderate scores) – 50–79%
• Red (low scores) – Below 50%

Job Fit (Match Type)

The Five Fit Levels

How Job Fit Is Determined

1. A Job Profile defines the ideal behavioral characteristics for the role across multiple dimensions
2. The candidate completes a behavioral assessment
3. Each dimension is scored and compared to the target
4. The overall fit level is calculated based on:
   • Number of dimensions that meet or exceed the target
   • Severity of any gaps
   • Overall pattern consistency (distortion detection)

Visual Indicators

In the applicant list, Job Fit appears as a colored chip:

• Each fit level has a distinct icon for accessibility
• Chips show the fit label text (e.g., "Strong Fit", "Good Fit")
• The chip uses outlined style for a clean appearance

Smart Fingerprint Recommendation

How It Works

The Smart Fingerprint system is an AI layer that analyzes all available data for a candidate and produces a holistic recommendation. It considers:

• Resume match score
• Behavioral assessment results (Job Fit)
• Assessment performance
• Skills alignment
• Experience relevance
• Any red flags or exceptional strengths

Recommendation Levels

Reasoning

Each recommendation includes reasoning bullets explaining why the system arrived at its conclusion. Hover over the recommendation in the list view to see reasoning such as:

• "Strong alignment between skills and job requirements"
• "Below-target scores on key behavioral dimensions"
• "Limited relevant experience for seniority level"

Star Rating (1–5)

Purpose

The star rating is a manual score assigned by recruiters or hiring managers to capture subjective evaluation that automated scoring can't measure—like interview performance, cultural impression, or gut feeling.

How to Set

• In the list view: Click the star widget on any applicant row
• In the profile view: Click the star widget in the professional summary section
• Ratings save automatically

Using Ratings for Filtering

Filter by rating in Advanced Filters using numeric filters:

• Show only 4+ star candidates
• Exclude unrated candidates
• Sort by rating to surface top-rated candidates first

Assessment Scores

Per-Assessment Scoring

Each assessment produces its own score. Depending on the assessment type:

Cohort Standing

Some assessments include cohort standing which shows how a candidate performed relative to others who took the same assessment. This provides valuable context—a 75% score means different things if the average is 50% vs. 90%.

AI-Powered Analysis (Smart Recruiter)

How AI Analysis Scores Work

When you run a Smart Recruiter analysis, the AI scores each candidate on a 0–100 scale against your custom prompt. Results show:

• Match Score – How well the candidate matches your described criteria
• Match Strength – Categorized as Strong, Moderate, or Weak
• Reasoning – Bullet points explaining the match
• Key Skills – Highlighted skills relevant to your query
• Concerns – Any potential issues or gaps identified

Score Colors in AI Results

Ranking Candidates

Default Sort Order

By default, applicants are sorted by application date (newest first). However, you can leverage scores for ranking:

Sorting Options

Using Filters to Rank

Combine filters for a powerful ranking workflow:

1. Filter by Fit: Show only "Strong Fit" and "Good Fit" candidates
2. Sort by Resume Score: Highest scores first
3. Review top results: These are your best candidates based on both behavioral fit and resume match

Stage-Based Ranking

Use stage folders to create ranking contexts:

• New stage sorted by Resume Score → Prioritize which new applicants to review first
• Interview stage sorted by Rating → See how candidates ranked after interviews
• All stages filtered by Strong Fit → Find your best candidates regardless of where they are in the process

Using Scores in Batch Auto Reject

Scores directly power the Batch Auto Reject feature:

• Set a Resume Score Threshold – Candidates below this score are flagged
• Select Fit Levels to reject – Target Weak Fit and Distortion candidates
• Combine with YOE ranges – Further narrow by experience level

Interpreting Score Combinations

Tip: Never rely on a single score. The power of SmoothHiring is the combination of multiple scoring dimensions giving you a complete picture.

Related Pages

• Managing Candidates – Viewing and sorting candidates by score
• Candidate Screening – How screening feeds into scoring
• Batch Auto Reject – Using scores for automated decisions
• Smart Resume Search – Finding specific qualifications beyond scores