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CandidateSearchFields

Search Fields Object for Candidate (Beta)🔗︎

Candidate Info Search Fields🔗︎

Name 🔗︎ string

name🔗︎

Search for candidates by their name (first name and last name). Example query: "name:John Doe"

Title 🔗︎ string

title🔗︎

Search for candidates by their title. Example query: "title:Mr"

Gender 🔗︎ integer

gender🔗︎

Search for candidates by gender. The genders are normalized to Textkernel Gender codes. Example query: gender:1

Location 🔗︎ string

location🔗︎

Search for candidates by their primary location. It is possible to Search for candidates by city or postal codes. For a more precise location search, add the ISO country code before the city name or postal code. When searching by city name in the US, it is recommended to add the ISO state code after the city name. Example queries: location:"New York", location:"US New York", location:"US New York NY", location:"10001", location:"US 10001"

Region 🔗︎ string

region🔗︎

Search for candidates by ISO 3166-2 region codes. Some exceptions to the subdivisions used by this field:

  • United States: this field contain US state, see ISO 3166-2:US.
  • France: this field contains the "département" and not the French administrative unit called "région" in France. It also includes the French overseas departments and the overseas territories as regions of France. See ISO 3166-2:FR.
  • United Kingdom of Great Britain and Northern Ireland: the regions are England GB-ENG, Scotland GB-SCT, Ireland GB-NIR and Wales GB-WLS. Use the subregion field for a more specific level. See ISO 3166-2:GB.

Example queries: region:"US-AL", region:"FR-16"

Subregion 🔗︎ string

subregion🔗︎

Search for candidates by ISO 3166-2 sub-region codes. For the United States, this field is not populated. Example queries: subregion:"GB-MAN", subregion:"FR-67"

Country 🔗︎ string

country🔗︎

Search for candidates by ISO 3166-2 country codes. Example query: country:"US"

PhoneNumbers 🔗︎ string[]

phonenumbers🔗︎

Search for candidates by any of their phone numbers. Phone numbers are normalized to the international format, including the international calling prefix. Example query: phone_numbers:"+17173856138"

HomePhoneNumbers 🔗︎ string[]

homephonenumbers🔗︎

Search for candidates by home phone numbers. Phone numbers are normalized to the international format, including the international calling prefix. Example query: home_phone_numbers:"+17173856138"

MobilePhoneNumbers 🔗︎ string[]

mobilephonenumbers🔗︎

Search for candidates by any of their phone numbers. Phone numbers are normalized to the international format, including the international calling prefix. Example query: mobile_phone_numbers:"+17173856138"

WorkPhoneNumbers 🔗︎ string[]

workphonenumbers🔗︎

Search for candidates by home phone numbers. Phone numbers are normalized to the international format, including the international calling prefix. Example query: work_phone_numbers:"+17173856138"

Email 🔗︎ string

email🔗︎

Search for candidates by their email address. Example query: email:"john.doe@example.com"

PersonalWebsite 🔗︎ string

personalwebsite🔗︎

While it is possible to search for candidates using their personal website URL, this field is mainly intended to display that information in the results. Example query: personal_website:"https://johndoe.example.com"

LinkedInUrl 🔗︎ string

linkedinurl🔗︎

While it is possible to search for candidates using their LinkedIn URL, this field is mainly intended to display that information in the results. Example query: linkedin_url:"https://linkedin.com/in/johndoe123"

XUrl 🔗︎ string

xurl🔗︎

While it is possible to search for candidates using their X (formerly Twitter) URL, this field is mainly intended to display that information in the results. Example query: x_url:"https://x.com/johndoe123"

XingUrl 🔗︎ string

xingurl🔗︎

While it is possible to search for candidates using their Xing URL, this field is mainly intended to display that information in the results. Example query: xing_url:"https://xing.com/profile/John_Doe123"

ViadeoUrl 🔗︎ string

viadeourl🔗︎

While it is possible to search for candidates using their Viadeo URL, this field is mainly intended to display that information in the results. Example query: viadeo_url:"https://viadeo.com/en/profile/johndoe123"

FacebookUrl 🔗︎ string

linkedinurl🔗︎

While it is possible to search for candidates using their Facebook URL, this field is mainly intended to display that information in the results. Example query: facebook_url:"https://facebook.com/johndoe123"

BirthDate 🔗︎ string

birthdate🔗︎

Search for candidates by their date of birth, in the format YYYY-MM-DD. Example query: birth_date:"1980-01-31"

BirthPlace 🔗︎ string

birthplace🔗︎

Search for candidates by their birth location. This field only support exact string match, it does not support location search by postal codes or radius search. Example query: birth_place:"New York"

Nationality 🔗︎ string

nationality🔗︎

Search for candidates by the ISO 3166-1 alpha-2 of their nationality. Example query: nationality:"FR"

NationalId 🔗︎ string[]

nationalid🔗︎

Search for candidates by their national ID. This field is currently extracted only for Spain, Chile, Colombia, South Africa and Israel. Example query: national_id:"12345678 W"

DriverLicenses 🔗︎ string[]

nationalid🔗︎

Search for candidates by their driver's licenses. Example query: drivers_licenses:"A"

CandidateStatus 🔗︎ string

candidatestatus🔗︎

Search for candidates by their status. Example queries: candidate_status:"Available", candidate_status:1

AvailabilityDate 🔗︎ string

availabilitydate🔗︎

Search for candidates by their availability date. Example query: availability_date:>=2024-06-01

HoursPerWeek 🔗︎ integer

hoursperweek🔗︎

Search for candidates by their available hours per week. Example query: hours_per_week:=>24

WillingToWorkRemotely 🔗︎ boolean

willingtoworkremotely🔗︎

Search for candidates that are willing to work remotely. Example query: willing_to_work_remotely:true

WillingToRelocate 🔗︎ boolean

willingtorelocate🔗︎

Search for candidates that are willing to relocate. Example query: willing_to_relocate:true

DesiredLocations 🔗︎ string[]

desiredlocations🔗︎

Search for candidates by their desired relocation locations. It is possible to Search for candidates by city or postal codes. For a more precise location search, add the ISO country code before the city name or postal code. When searching by city name in the US, it is recommended to add the ISO state code after the city name. Example queries: desired_locations:"New York", desired_locations:"US New York", desired_locations:"US New York NY", desired_locations:"10001", desired_locations:"US 10001"

AllLocations 🔗︎ string[]

alllocations🔗︎

Search for candidates by all their locations, primary and relocation locations. It is possible to Search for candidates by city or postal codes. For a more precise location search, add the ISO country code before the city name or postal code. When searching by city name in the US, it is recommended to add the ISO state code after the city name. Example queries: all_locations:"New York", all_locations:"US New York", all_locations:"US New York NY", all_locations:"10001", all_locations:"US 10001"

DesiredContractTypes 🔗︎ string[]

desiredcontracttypes🔗︎

Search for candidates by their desired contract types. Example queries: desired_contract_types:"Full-time"

DesiredContractHours 🔗︎ object

desiredcontracthours🔗︎

Search for candidates by their desired contract hours. This field supports to 2 sub-fields: - min (type integer) - max (type integer)

Example queries: desired_contract_hours.min:>=24, desired_contract_hours:{min:>=24 max:=<32}

Skills And Certifications Search Fields🔗︎

LanguageSkills 🔗︎ string[]

languageskills🔗︎

Search candidates by their normalized language skills, normalized to the Textkernel Skills Taxonomy codes. Example query: language_skills:"KS123K75YYK8VGH90NCS"

ItSkills 🔗︎ string[]

itskills🔗︎

Search candidates by their IT skills, normalized to the Textkernel Skills Taxonomy codes. Example query: it_skills:"KS1200H6XYN1CR0G5NZ0"

ProfessionalSkills 🔗︎ string[]

professionalskills🔗︎

Search candidates by their professional skills, normalized to the Textkernel Skills Taxonomy codes. Example query: professional_skills:"KS1212B6QR5SK1LSD4S4"

SoftSkills 🔗︎ string[]

softskills🔗︎

Search candidates by their soft skills, normalized to the Textkernel Skills Taxonomy codes. Example query: soft_skills:"KSLSQ90KU2MTW9EUR1B5"

LanguageSkillsObject 🔗︎ object[]

languageskillsobject🔗︎

Search for candidates by their language skills, using additional sub-fields to filter by specific characteristics of the skills. This field includes the following sub-fields:

  • skill (type string): the language skill, as extracted from the resume;
  • skill_code (type code): the normalized language skill, mapped to the Textkernel Skills Taxonomy codes;
  • iso_code (type code): the normalized language skill, mapped to the ISO 639-1 codes;
  • level (type string): the proficiency level in the language, as extracted from the resume;
  • level_code (type code): the normalized proficiency level, mapped to the Textkernel Skill Level codes;
  • years (type string): the number of years of experience with the language skill.

Example queries: language_skills_object: { skill:"English" }, language_skills_object: { iso_code:"en" level_code:>=3 }

ItSkillsObject 🔗︎ object[]

itskillsobject🔗︎

Search for candidates by their IT skills, using additional sub-fields to filter by specific characteristics of the skills. This field includes the following sub-fields:

  • skill (type string): the specific IT skill, as extracted from the resume;
  • skill_code (type code): the normalized IT skill, mapped to the Textkernel Skills Taxonomy codes;
  • level (type string): not available for this skill type;
  • level_code (type code): not available for this skill type;
  • years (type string): the number of years of experience with the skill.

Example queries: it_skills_object:{ skill:"Python" years:>=5 }

ProfessionalSkillsObject 🔗︎ object[]

professionalskillsobject🔗︎

Search for candidates by their professional skills, using additional sub-fields to filter by specific characteristics of the skills. This field includes the following sub-fields:

  • skill (type string): the specific professional skill, as extracted from the resume;
  • skill_code (type code): the normalized professional skill, mapped to the Textkernel Skills Taxonomy codes;
  • level (type string): not available for this skill type;
  • level_code (type code): not available for this skill type;
  • years (type string): the number of years of experience with the skill.

Example queries: professional_skills_object:{ skill_code:"KS1212B6QR5SK1LSD4S4" }

SoftSkillsObject 🔗︎ object[]

softskillsobject🔗︎

Search for candidates by their soft skills, using additional sub-fields to filter by specific characteristics of the skills. This field includes the following sub-fields:

  • skill (type string): the specific soft skill, as extracted from the resume;
  • skill_code (type code): the normalized soft skill, mapped to the Textkernel Skills Taxonomy codes;
  • level (type string): not available for this skill type;
  • level_code (type code): not available for this skill type;
  • years (type string): the number of years of experience with the skill.

Example queries: soft_skills_object:{ skill:"Proactive" }

Education Search Fields🔗︎

EducationLevelLocal 🔗︎ string

educationlevellocal🔗︎

Search candidates by their education level normalized to Textkernel Local Education Level codes. Example query: education_level_local:"US_7"

EducationLevelInternational 🔗︎ string

educationlevelinternational🔗︎

Search candidates by their education level normalized to Textkernel International Education Level codes. Example query: education_level_international:3

Degrees 🔗︎ object[]

degrees🔗︎

Search candidates by their full education history, filtering by degree names, institutes and degree dates. This field includes the following sub-fields:

  • name (type string): the name of the degree;
  • degree_local (type code): the normalized degree level, mapped to the Textkernel Local Education Level codes;
  • degree_international (type code): the normalized degree level, mapped to the Textkernel International Education Level codes;
  • institute (type string): the institute issuing the degree;
  • city (type string): the location of the institute;
  • start_date (type date): the start date of the education item;
  • end_date (type date): the end date of the education item.

Example queries: degrees:{ name: "Bachelor in Computer Science" }, degrees:{ degree_international:3 end_date:<=today-365 }

Courses 🔗︎ object[]

courses🔗︎

Search candidates by their courses, filtering by course names, institutes and course dates. This field includes the following sub-fields:

  • name (type string): the name of the course;
  • institute (type texstringt): the institute issuing the course;
  • city (type string): the location of the institute;
  • start_date (type date): the start date of the course;
  • end_date (type date): the end date of the course.

Example queries: courses:{ name:"AWS Certified Solutions Architect" }

Work Experience Search Fields🔗︎

LastJobTitle 🔗︎ string

lastjobtitle🔗︎

Search candidates by the lastest job title. Example query: last_job_title:"sales manager"

JobTitles 🔗︎ string[]

jobtitles🔗︎

Search candidates by all their job titles. Example query: job_titles:"human resources adviser"

RecentJobTitles 🔗︎ string[]

recentjobtitles🔗︎

Search candidates by their job titles held in the last 4 years. Example query: recent_job_titles:"human resources adviser"

RecentProfessionCodes 🔗︎ int[]

recentprofessioncodes🔗︎

Search candidates by their recent (last 4 years) job titles, normalized to the Textkernel Profession codes. Example query: recent_profession_codes:11

RecentProfessionGroups 🔗︎ int[]

recentprofessiongroups🔗︎

Search candidates by their recent (last 4 years) job titles, normalized to the Textkernel Profession Group codes. Example query: recent_profession_groups:223

RecentProfessionClasses 🔗︎ int[]

recentprofessionclasses🔗︎

Search candidates by their recent (last 4 years) job titles, normalized to the Textkernel Profession Class codes. Example query: recent_profession_classes:19

Workfield 🔗︎ object

workfield🔗︎

Search candidates by their current work field, based on their latest job titles, normalized to the Textkernel Profession Group codes. Furthermore, it is possible to filter by the candidate´s experience level in the workfield. Experience levels are normalized to the Textkernel Experience Level codes.

This field includes the following sub-fields:

  • profession_group (type int): the current work field of the candidate;
  • experience_level (type int): the candidate´s experience in the work field.

Example query: workfield:{profession_group:223 experience_level:3}

LastEmployer 🔗︎ string

lastemployer🔗︎

Search candidates by the lastest employer. Example query: last_employer:"Acme"

Employers 🔗︎ string[]

employers🔗︎

Search candidates by their employers. Example query: employers:"Acme"

RecentEmployers 🔗︎ string[]

recentemployers🔗︎

Search candidates by their recent (last 4 years) employers. Example query: recent_employers:"Acme"

ExperienceYears 🔗︎ integer

experienceyears🔗︎

Search candidates by their total years of experience, calculated based on the work history mentioned in the resume. Example query: experience_years:>=5

Experiences 🔗︎ object[]

experiences🔗︎

Search candidates by their full work history, filtering by job titles, employer names and employment dates. This field includes the following sub-fields:

  • job_title (type string): the job title of the position;
  • employer (type string): the name of the employer;
  • city (type string): the location of the job, which may differ from the employer's location;
  • start_date (type date): the start date of the work experience;
  • end_date (type date): the end date of the work experience.

Example queries: experiences:{ job_title:"sales manager" employer:"Acme" }