AI-Powered Recruitment Matching Events

This dataset provides detailed records of AI-powered candidate-to-job matching events, including demographic data, job attributes, match scores, and feedback. Designed for fairness audits and recruitment pipeline optimization, it enables analysis of AI model performance, bias detection, and process improvement in talent acquisition.

  • last updated 5 Sept 2025
  • by GoMask
The brief that made it

Fairness and bias audits of AI recruitment systems

Sample rows

preview · 8 of 200 rows · all 23 columns
match_idstringmatch_statusstringmatch_scorefloatfeedback_providedbooleancandidate_genderstringcandidate_idstringjob_idstringmatch_datetimedatetimeai_model_versionstringcandidate_ethnicitystringcandidate_ageintegercandidate_location_citystringcandidate_location_statestringcandidate_location_countrystringjob_titlestringjob_departmentstringjob_location_citystringjob_location_statestringjob_location_countrystringjob_salary_minfloatjob_salary_maxfloatfeedback_scorefloatfeedback_commentsstring
B3K8HX9Prejected0.12falsemalecand_10001job_200012022-07-15T10:23:00v1.1.8White19AustinTXUSARetail Store AssociateCustomer ServiceAustinTXUSA1850024500blankblank
Z7N2QJ6Cpending0.84falsefemalecand_10002job_200022022-11-11T16:09:00v1.2.4Asian26San JoseCAUSASoftware Engineer IEngineeringSan JoseCAUSA82000110000blankblank
J2M3R8XKaccepted0.57truenon-binarycand_10003job_200032023-01-13T13:35:00v1.3.0Black34TorontoONCanadaData AnalystAnalyticsTorontoONCanada52000740004.5Candidate was well suited for the position, strong skills exhibited.
W5L9T1VQpending0.39falsefemalecand_10004job_200042023-03-28T09:44:00v1.2.9Hispanic21PhoenixAZUSAWarehouse AssistantLogisticsPhoenixAZUSA2300031500blankblank
S6D2F3KQaccepted0.99truemalecand_10005job_200052023-05-02T14:37:00v1.3.1South Asian33DelhiDelhiIndiaBusiness Intelligence SpecialistBusiness IntelligenceDelhiDelhiIndia900001400005Exceptional match, candidate exceeded expectations.
Q4Y8H2N9withdrawn0.65falseprefer-not-to-saycand_10006job_200062023-07-07T11:22:00v2.0.0Native American48HonoluluHIUSAEvents CoordinatorEventsHonoluluHIUSA5500082000blankblank
T8C1P7LZaccepted0.88truefemalecand_10007job_200072023-10-19T15:17:00v2.2.0White29LondonLondonUnited KingdomProduct DesignerDesignLondonLondonUnited Kingdom64000990004.5Creative input and skills matched the job requirements.
H3U9E2SWaccepted0.77truefemalecand_10008job_200082024-01-05T18:55:00v2.3.0Pacific Islander28HonoluluHIUSACustomer Service LeadCustomer ServiceHonoluluHIUSA42000560004Strong communication skills, positive team feedback.

What the 200 rows show

from the 200-row sample

Accepted (match status) stands out: 73 of its 73 rows have feedback_provided = true, against 1 of 127 for the rest.

  • 37%feedback_provided = true
  • 0.71median match_score
  • 10candidate location countries
  • 10job location countries
  • 13candidate ethnicities
  • 18ai model versions
Feedback provided rate by match_statusfeedback_provided = true
0%50%100%2%pending1 of 58100%accept…73 of 730%reject…0 of 330%expired0 of 190%withdr…0 of 17
match_score200 rows, in bands of 0.1
0204014182099102027403300.51match_score →

Median 0.71, from 0.0 to 1.0.

candidate_gender197 rows with a value · 3 left blank
  1. female100
  2. male69
  3. non-binary12
  4. prefer-not-to-say11
  5. other5
23 columns by typefrom the column list below
  • string 16
  • integer 1
  • float 4
  • datetime 1
  • boolean 1

Columns

23 columns in four groups
blueprint · 23 columns
columntypedescriptionexample
Text 16 columns
match_idstringUnique identifier for each candidate-job match event generated by the AI system.uniqueB3K8HX9P
candidate_idstringUnique identifier for the candidate involved in the match.cand_10001
job_idstringUnique identifier for the job posting involved in the match.job_20001
ai_model_versionstringVersion of the AI model used to generate the match.v1.1.8
candidate_genderstringGender of the candidate, if available, for fairness auditing.male · female · non-binary · prefer-not-to-say · other · optionalmale
candidate_ethnicitystringEthnicity of the candidate, if available, for fairness auditing.optionalWhite
candidate_location_citystringCity of the candidate's primary location.optionalAustin
candidate_location_statestringState or province of the candidate's primary location.optionalTX
candidate_location_countrystringCountry of the candidate's primary location.10 countries · optionalUSA
job_titlestringTitle of the job posting.Retail Store Associate
job_departmentstringDepartment or functional area for the job posting.optionalCustomer Service
job_location_citystringCity where the job is located.optionalAustin
job_location_statestringState or province where the job is located.optionalTX
job_location_countrystringCountry where the job is located.10 countries · optionalUSA
match_statusstringCurrent status of the match (e.g., pending, accepted, rejected, expired).pending · accepted · rejected · expired · withdrawnrejected
feedback_commentsstringTextual feedback comments, if provided.optionalCandidate was well suited…
Numbers 5 columns
match_scorefloatNumerical score representing the strength or quality of the match as determined by the AI system (typically 0-1 or 0-100).0 to 10.12
candidate_ageintegerAge of the candidate at the time of matching, if available.16 to 100 · optional19
job_salary_minfloatMinimum salary offered for the job, if specified.0 or more · optional18500
job_salary_maxfloatMaximum salary offered for the job, if specified.0 or more · optional24500
feedback_scorefloatNumerical score from feedback, if provided (e.g., 1-5 scale).1 to 5 · optional4.5
Dates and times 1 column
match_datetimedatetimeTimestamp when the AI system generated the candidate-job match.2022-07-15T10:23:00
True or false 1 column
feedback_providedbooleanIndicates whether feedback was provided by the candidate or employer regarding the match.optionalfalse

Use it for

  • feedback provi…37%74 of 200 rowsmean match score by m…0.69pend…0.86acce…0.23reje…0.28expi…

    A human resources dashboard

    The feedback_provided rate, match_score by match_status and a breakdown of candidate_gender. Excel, Power BI or Tableau.

  • Why do 74 of 200 rows have feedback_provided = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable matches with candidate_id, job_id and match_datetime to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 23 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows23 columns
Yours1,000 rows23 columnscandidate_location_city: UK only

blueprint · ai-powered-recruitment-matching-events

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 23 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Candidate and job IDs included.
  • Match score (0-1) mandatory.
  • Date of matching event required.
  • Stage in recruitment pipeline flagged.
  • Previous match attempts counted.
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
ai-powered-recruitment-matching-events

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