hired.matching#

Job matching and scoring utilities.

Match candidate profiles against job postings to identify the best opportunities.

Functions

quick_match(candidate_skills, jobs[, top_n])

Quick utility function to match jobs against candidate skills.

Classes

`JobMatcher`([candidate_skills, ...])	Match candidate profiles against job postings.
`MatchScore`(job, overall_score, ...[, ...])	Score for a job match.

class hired.matching.JobMatcher(candidate_skills=None, candidate_keywords=None, required_skills=None, min_salary=None, max_salary=None, preferred_locations=None, remote_only=False)[source]#

Bases: object

Match candidate profiles against job postings.

Examples

>>> matcher = JobMatcher(candidate_skills=['python', 'django', 'aws'])
>>> scores = matcher.score_jobs(job_results)
>>> top_jobs = matcher.get_top_matches(job_results, n=10)

filter_jobs(jobs, min_score=50.0)[source]#

Filter jobs that meet minimum score threshold.

Parameters:

jobs (List[JobResult]) – List of JobResult objects
min_score (float) – Minimum score threshold (0-100)

Return type:

List[JobResult]

Returns:

List of JobResult objects that meet threshold

get_recommendations(jobs, top_n=5)[source]#

Get human-readable recommendations based on job matches.

Parameters:

jobs (List[JobResult]) – List of JobResult objects
top_n (int) – Number of top jobs to include in recommendations

Return type:

str

Returns:

Formatted recommendation string

get_top_matches(jobs, n=10, min_score=0.0)[source]#

Get top N job matches sorted by score.

Parameters:

jobs (List[JobResult]) – List of JobResult objects
n (int) – Number of top matches to return
min_score (float) – Minimum score threshold (0-100)

Return type:

List[MatchScore]

Returns:

List of MatchScore objects sorted by overall_score descending

identify_skill_gaps(jobs)[source]#

Identify skills frequently requested but not possessed by candidate.

Parameters:: jobs (List[JobResult]) – List of JobResult objects to analyze
Returns:: frequency} for missing skills
Return type:: Dict[str, int]

score_job(job)[source]#

Score a single job against candidate profile.

Parameters:: job (JobResult) – JobResult to score
Return type:: MatchScore
Returns:: MatchScore object

score_jobs(jobs)[source]#

Score multiple jobs.

Parameters:: jobs (List[JobResult]) – List of JobResult objects
Return type:: List[MatchScore]
Returns:: List of MatchScore objects

class hired.matching.MatchScore(job, overall_score, skill_match_score, keyword_match_score, matched_skills=<factory>, missing_skills=<factory>, matched_keywords=<factory>, compensation_match=None, location_match=None)[source]#

Bases: object

Score for a job match.

get_summary()[source]#

Get human-readable summary.

Return type:: str

to_dict()[source]#

Convert to dictionary.

Return type:: Dict[str, Any]

hired.matching.quick_match(candidate_skills, jobs, top_n=10)[source]#

Quick utility function to match jobs against candidate skills.

Parameters:

candidate_skills (List[str]) – List of candidate’s skills
jobs (List[JobResult]) – List of JobResult objects
top_n (int) – Number of top matches to return

Return type:

List[MatchScore]

Returns:

List of top MatchScore objects

Examples

>>> from hired import JobSources, SearchCriteria
>>> from hired.matching import quick_match
>>>
>>> sources = JobSources()
>>> jobs = sources.jobspy.search(SearchCriteria(query="python developer"))
>>>
>>> matches = quick_match(
...     candidate_skills=['python', 'django', 'postgresql'],
...     jobs=jobs,
...     top_n=5
... )
>>>
>>> for match in matches:
...     print(match.get_summary())