Match
The Match object includes an evaluation and explanation of the fit between a Candidate and a Job. This includes a match score and category, and a Match Report with supporting/explanatory details. The Match Report includes:
- "attribute scores" for work history, skills, and education,
- a skills section detailing the candidate's competence for the job's key technical and baseline skills,
- an education section detailing whether the candidate meets the job's basic degree level requirements,
- a certifications section* detailing whether the candidate meets the job's education requirements, and
- a narrative explanation* of the match assessment.
*Certifications section and match narrative are currently in beta and not included in the standard Match Report.
Match Schema
Match Score
Each match is scored using a numerical match score between 0 and 1 and a match score category: Very High, High, Medium, Low. See Descriptions of Match Score Categories for category details.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
match_score | float | Numerical assessment between 0 and 1 of the match between a candidate and a job. |
match_score_category | string | Categorical description of the match score. |
Match Report
The Match Report explains the match score, providing details on the assessed fit in work history, skills, education, and certifications.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
attributes | object | A set of attribute scores for the candidate. See below for schema details. |
skills | object | Skill section for both technical and baseline skills. |
education | object | A comparison between the education level required for the job and held by the candidate. |
certifications | object | A comparison between the certifications requested in the job description and held by the candidate. |
Attribute Scores
To better explain the overall match assessment to end users, the match score is segmented into attributes that assess the candidate’s fit based on skills, work experience, and education. See Descriptions of Match Score Categories for category details.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
skills_category | string | Categorical value assessing candidate's skill fit for a role. (Values include Very High, High, Medium, and Low) |
work_history_category | string | Categorical value assessing candidate's work experience and seniority fit for a role. |
education_category | string | Categorical value assessing candidate's education fit for a role. |
Skills Section
The Skills section assesses the candidate's competence for the job's key technical and baseline skills. AdeptID identifies key skills for the job based on the skills listed in the job description and AdeptID's advanced skill inference models (which predict what skills are required based on the job title and other attested skills). Similarly, candidate competence is based on both attestation in resumes and inference from other skills and work experience. The skill taxonomy section includes additional information about skills and their associated metadata. See Descriptions of Match Score Categories for category details.
In the Skills section, skills are grouped into technical and baseline skills.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
technical | array of objects | List of Skill objects for key technical skills. See below for schema details. |
baseline | array of objects | List of Skill objects for key baseline skills. See below for schema details. |
Skill Object
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
rank | int | The ranked importance of the skill for the job, with 0 being the most important. |
skill_name | string | Skill name. |
skill_cluster | string | Name of a group of similar skills. This can be used for organizing lists of skills. |
attested_job | boolean | Whether or not the skill was listed in the job description. |
attested_candidate | boolean | Whether or not the skill was listed in the candidate profile/resume. |
assessment_category | enum | Categorical description of the candidate's skill competency. |
Education Section
The education section compares the minimum degree level required by the job with the highest degree level attained by the candidate.
Note: The education_category attribute score considers the relevance of the candidate's program(s) of study to the job in addition to the achieved degree level(s). Thus, in some scenarios, a candidate may meet or exceed a job's minimum degree requirement but receive a "Low" education attribute score.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
job_degree_min | enum | The minimum degree level required by the job. |
candidate_degree_max | enum | The highest degree level attained by the candidate. |
meets_requirement | enum | Whether the candidate does not meet, meets, or exceeds the requirement. |
Certifications Section
The certifications section compares the job-required certifications with those held by the candidate. It also lists additional certifications held by the candidate but not required by the job.
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
job_required | array of objects | List of Certification objects for job-required certifications. |
candidate_other | array of strings | List of additional certifications held by the candidate. |
Certification Object
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
name | string | Certification name. |
attested_candidate | boolean | Whether or not the certification was listed in the candidate profile/resume. |
meets_requirement | enum | Whether the candidate does not meet, meets, or exceeds the requirement. |
Match Narrative
The match narrative provides a Generative AI-created synopsis of the match, drawing from the details in the Match Report. (Available in Beta)
| FIELD | TYPE | SHORT DESCRIPTION |
|---|---|---|
narrative | string | Generative AI-created synopsis of the match. |
Category Definitions
Descriptions of Match Categories
The following descriptions apply to Match Score, Attribute Scores, and the Skills Section.
| CATEGORY | FROM | TO | DESCRIPTION |
|---|---|---|---|
| VERY HIGH | 0.9 | 1.0 | Outstanding matches warranting immediate action (an application, an interview, etc.). Typically ~20% of matches for large candidate pools. |
| HIGH | 0.8 | <0.9 | Good matches warranting action (an application, an interview, etc.), with small and addressable gaps. Typically ~30% of matches for large candidate pools. |
| MEDIUM | 0.7 | <0.8 | Decent matches that can potentially warrant action (an application, an interview, etc.) when Very High or High matches do not exist or are in short supply. These matches have multiple gaps/shortcomings, but also multiple strengths. Typically ~25% of matches for large candidate pools. |
| LOW | 0.0 | <0.7 | Poor matches with many gaps/shortcomings that do not warrant action (an application, an interview, etc.). Typically ~25% of matches for large candidate pools. |
Mapping Skill Competencies to Skill Overlaps and Gaps
Some organizations choose to present skill competency assessments as overlaps and gaps for the job. The below table provides a mapping between competency categories (assessment_category) in the Skills Section and an overlap/gap-oriented presentation to users.
| CATEGORY | MAPPING | DESCRIPTION |
|---|---|---|
| VERY HIGH | LARGE OVERLAP | Large Skill Overlap: A candidate almost certainly possesses direct experience with the relevant skill. This should be considered a highly positive indicator for their application. |
| HIGH | OVERLAP | Skill Overlap: A candidate likely possesses direct experience with, or adjacent and transferable skills to, the relevant skill. This should be considered a positive indicator for their application. |
| MEDIUM | NEUTRAL | Neutral: It is unclear the degree to which the candidate possesses direct experience with, or adjacent and transferable skills to, the relevant skill. This should not be considered a positive or negative indicator for their application. |
| LOW | GAP | Skill Gap: A candidate almost certainly lacks direct experience with, or adjacent and transferable skills to, the relevant skill. This should be considered a negative indicator for their application. |
Match Example
{
"candidates_scores": {
"Piya Gupta": {
"match_score": 0.8164023756980896,
"match_score_category": "High",
"match_report": {
"attributes": {
"education_category": "Low",
"skills_category": "High",
"work_history_category": "High"
},
"skills": {
"technical": [
{
"rank": 0,
"skill_name": "Marketing",
"skill_cluster": "Marketing Strategy and Techniques",
"attested_job": true,
"attested_candidate": true,
"assessment_category": "Very High"
},
{
"rank": 1,
"skill_name": "Social Media",
"skill_cluster": "Social Media",
"attested_job": false,
"attested_candidate": true,
"assessment_category": "Very High"
},
{
"rank": 2,
"skill_name": "Marketing Communications",
"skill_cluster": "Communication",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Medium"
},
{
"rank": 3,
"skill_name": "Digital Marketing",
"skill_cluster": "Digital Marketing",
"attested_job": true,
"attested_candidate": false,
"assessment_category": "Medium"
},
{
"rank": 4,
"skill_name": "Marketing Materials",
"skill_cluster": "Promotions and Campaigns",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 5,
"skill_name": "Marketing Strategies",
"skill_cluster": "Marketing Strategy and Techniques",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Low"
},
{
"rank": 6,
"skill_name": "Lead Generation",
"skill_cluster": "Prospecting and Qualification",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Low"
},
{
"rank": 7,
"skill_name": "Marketing Management",
"skill_cluster": "Marketing Strategy and Techniques",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Low"
},
{
"rank": 8,
"skill_name": "Content Creation",
"skill_cluster": "Content Development and Management",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Medium"
},
{
"rank": 9,
"skill_name": "Proofreading",
"skill_cluster": "Writing and Editing",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Medium"
}
],
"baseline": [
{
"rank": 0,
"skill_name": "Self-Motivation",
"skill_cluster": "Initiative and Leadership",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 1,
"skill_name": "Communication",
"skill_cluster": "Communication",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 2,
"skill_name": "Verbal Communication Skills",
"skill_cluster": "Communication",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 3,
"skill_name": "Time Management",
"skill_cluster": "Initiative and Leadership",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 4,
"skill_name": "Ability To Meet Deadlines",
"skill_cluster": "Initiative and Leadership",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 5,
"skill_name": "Interpersonal Communications",
"skill_cluster": "Communication",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 6,
"skill_name": "Planning",
"skill_cluster": "Initiative and Leadership",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "High"
},
{
"rank": 7,
"skill_name": "Problem Solving",
"skill_cluster": "Critical Thinking and Problem Solving",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 8,
"skill_name": "Prioritization",
"skill_cluster": "Project Management",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "Very High"
},
{
"rank": 9,
"skill_name": "Microsoft Office",
"skill_cluster": "Office and Productivity Equipment and Technology",
"attested_job": false,
"attested_candidate": false,
"assessment_category": "High"
}
]
},
"education": {
"job_degree_min": "BACHELORS",
"candidate_degree_max": "ASSOCIATES",
"meets_requirement": "Does not meet"
}
}
}
}
}
Updated about 14 hours ago