Overview

This page details the Person Data that we provide through our Person APIs, such as Person Enrichment and Person Search.

Field AvailabilityNot all fields are available in all bundles.Free plans, by default, do not have access to contact fields like emails, phone numbers, and street addresses and will instead appear as true if the value exists or false if it does not. To unlock the values, please upgrade to a Pro plan. Read more here: Plan types: Free vs Pro

For more information about data formatting, see Data Types and Data Formatting
For a full example record, see Example Person Record.
For a simplified overview of our person fields, check out the Person Data Overview.
For more details about our person fields, including fill rates and which fields are included in the base vs premium field bundles, check out our Person Stats pages.
For a full data ingestion JSON schema, check out this page.
If you’d like access to premium fields or have questions about which fields are included in your specific field bundle(s), please speak to one of our data consultants.

Identifiers

`first_name`


Data Type	`String`
Description	The person’s first name.

Field Details

The person’s first name.

Example

JSON

  "first_name": "sean"

`full_name`


Data Type	`String`
Description	The person’s full name.

Field Details

The first and the last name fields appended with a space.

Example

JSON

  "full_name": "sean thorne"

`id`


Data Type	`String`
Description	A unique persistent identifier for the person.

Field Details

The ID is a unique, persistent, and hashed value that represents a specific person. As of v24, IDs have a max length of 64 characters, although in practice we expect IDs to be closer to 32 characters in length. See Persistent IDs for more information.

Example

JSON

  "id": "qEnOZ5Oh0poWnQ1luFBfVw_0000"

`last_initial`


Data Type	`String (1 character)`
Description	The first letter of the person’s last name.

Field Details

The first letter of the person’s last name.

Example

JSON

  "last_initial": "t"

`last_name`


Data Type	`String`
Description	The person’s last name.

Field Details

The person’s last name.

Example

JSON

  "last_name": "thorne"

`middle_initial`


Data Type	`String (1 character)`
Description	The first letter of the person’s middle name.

Field Details

The first letter of the person’s middle name.

Example

JSON

  "middle_initial": "f"

`middle_name`


Data Type	`String`
Description	The person’s middle name.

Field Details

The person’s middle name.

Example

JSON

  "middle_name": "fong"

`name_aliases`


Data Type	`Array [String]`
Description	Any other names the person goes by.

Field Details

Any associated names or aliases besides the primary one used in the full_name field.

Sort OrderName aliases are sorted with the primary alias first. The remaining aliases are sorted by num_sources, last_seen, first_seen, full_name, all in reverse order (highest first, most recent, Z→A):

Primary name first
num_sources (highest first)
last_seen (most recent first)
first_seen (most recent first)
full_name (Z→A)

Example

JSON

  "name_aliases": [
    "andrew nichol",
    "r andrew nichol",
    "robert nichol"
  ]

Contact Information

`emails`


Data Type	`Array [Object]`
Description	Email addresses associated with the person.

Field Details

Note This array contains historical email addresses and should not directly be used for email outreach. Recommended Alternatives

Each email associated with the person will be added to this list as its own object.

Field	Data Type	Description
`address`	`String`	The fully parsed email address
`first_seen`	`String (Date)`	The date that this entity was first associated with the Person record.
`last_seen`	`String (Date)`	The date that this entity was last associated with the Person record.
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this entity with the Person record.
`md5_hash`	`String`	A 128-bit hash of an email in md5 format.
`sha_256_hash`	`String`	A 256-bit hash of an email in sha256 format.
`type`	`Enum (String)`	The type of email address. Must be one of our Canonical Email Types

Sort OrderEmails are sorted first by last_seen, then by first_seen, email , all in reverse order (most recent first, Z→A):

last_seen (most recent first)
first_seen (most recent first)
email (Z→A)

Example

JSON

  "emails": [
    {
      "address": "sean@peopledatalabs.com",
      "type": "current_professional",
      "md5_hash": "89eb6bc60e92f3d6ffbb4e7b4d15cacd",
      "sha_256_hash": "138ea1a7076bb01889af2309de02e8b826c27f022b21ea8cf11aca9285d5a04e",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    },
    {
      "address": "sean@gmail.com",
      "type": "personal",
      "md5_hash": "17725f5327de7695d658f124636cbd23",
      "sha_256_hash": "ae0591a02b4cb0be73fff1ebe061a95b1bfc23350a9a297923edda014c6a88f8",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    }
  ]

`mobile_phone`


Data Type	`String (Phone)`
Description	The personal mobile phone associated with this individual. Mobile phones can only be associated with 1 person in the PDL data.

Field Details

The mobile_phone field is generated from a highly confident source of mobile phones. We’ve hand-validated a sample of these and seen over 90% accuracy.

Example

JSON

  "mobile_phone": "+15558675309"

`personal_emails`


Data Type	`Array [String]`
Description	All personal emails associated with the person.

Field Details

The list of all emails tagged as type = personal.

Sort OrderPersonal emails are sorted with the recommended personal email first. The remaining emails are sorted in the same order as the emails array:last_seen, then by first_seen, email, all in reverse order (most recent first, Z→A):

Recommended personal email first
last_seen (most recent first)
first_seen (most recent first)
email (Z→A)

Example

JSON

  "personal_emails": [
    "sean@gmail.com"
  ]

`phone_numbers`


Data Type	`Array [String (Phone)]`
Description	All phone numbers associated with the person.

Field Details

For more detailed metadata on individual phone numbers, see the phones field.

Sort OrderPhone numbers are sorted with any mobile phone numbers first. The rest of the array is sorted by num_sources, last_seen, first_seen, all in reverse order and using the E.164 format (highest first, most recent first):

Mobile phone numbers first
num_sources (highest first)
last_seen (most recent first)
first_seen (most recent first)

Example

JSON

  "phone_numbers": [
    "+15558675309"
  ]

`phones`


Data Type	`Array [Object]`
Description	The list of phone numbers associated with this record with additional metadata.

Field Details

Each phone number object in this list will contain the following information.

Field	Data Type	Description
`first_seen`	`String (Date)`	The date that this number was first associated with this record.
`last_seen`	`String (Date)`	The date that this number was last associated with this record.
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this profile with this record.
`number`	`String (Phone)`	The phone number.

Sort OrderPhones are sorted with any mobile phone numbers first. The rest of the array is sorted by num_sources, last_seen, first_seen, all in reverse order and using the E.164 format (highest first, most recent first):

Mobile phone numbers first
num_sources (highest first)
last_seen (most recent first)
first_seen (most recent first)

Example

JSON

  "phones": [
    {
      "number": "+15558675309",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    }
  ]

`recommended_personal_email`


Data Type	`String`
Description	The best available email to reach a person.

Field Details

This field is generated by analyzing the all of a person’s emails in the personal_emails list to identify the best available email. Through testing, we’ve found that using the email identified in recommended_personal_email versus selecting a random email address from personal_emails resulted in ~37% higher deliverability.

Example

JSON

  "recommended_personal_email": "sean@gmail.com"

`work_email`


Data Type	`String`
Description	The person’s current work email.

Field Details

The value for this field must use valid email address formatting. It is common and expected that work email domains may differ from the company’s website for a number of reasons:

The company changed their website domain
The company has opted for a shorter email domain
The company has been merged into or was acquired by another company

Example

JSON

  "work_email": "sean@peopledatalabs.com"

Current Company

These fields describe the company the person currently works at. These fields will match the corresponding values in our Company Schema and will use the same formatting and parsing logic.

`job_company_12mo_employee_growth_rate`


Data Type	`Float`
Description	The person’s current company’s percentage increase in total headcount over the past 12 months. Mapped from `employee_growth_rate.12_month`. Growth rate is calculated as `(current_employee_count / previous_employee_count) - 1`.

Example

JSON

  "job_company_12mo_employee_growth_rate": -0.1379

`job_company_facebook_url`


Data Type	`String`
Description	The person’s current company’s Facebook URL.

Example

JSON

  "job_company_facebook_url": "facebook.com/peopledatalabs"

`job_company_founded`


Data Type	`Integer (> 0)`
Description	The person’s current company’s founding year.

Example

JSON

  "job_company_founded": 2015

`job_company_employee_count`


Data Type	`Integer (>= 0)`
Description	The total number of PDL profiles associated with the person’s current company. Mapped from `employee_count`.

Example

JSON

  "job_company_employee_count": 125

`job_company_id`


Data Type	`String`
Description	The person’s current company’s PDL ID.

Example

JSON

  "job_company_id": "tnHcNHbCv8MKeLh92946LAkX6PKg"

`job_company_industry`


Data Type	`Enum (String)`
Description	The person’s current company’s industry.

Example

JSON

  "job_company_industry": "computer software"

`job_company_industry_v2`


Data Type	`Enum (String)`
Description	The v2 industry for the person’s current company.

Example

JSON

  "job_company_industry_v2": "internet marketplace platforms"

Details

Industry v2 is the self-reported industry from an expanded list of Canonical V2 Industries. If no industry is found, the field will be null

`job_company_inferred_revenue`


Data Type	`Enum (String)`
Description	The estimated annual revenue range in USD of the person’s current company.

Example

JSON

  "job_company_inferred_revenue": "$25M-$50M"

`job_company_linkedin_id`


Data Type	`String`
Description	The person’s current company’s LinkedIn ID.

Example

JSON

  "job_company_linkedin_id": "18170482"

`job_company_linkedin_url`


Data Type	`String`
Description	The person’s current company’s LinkedIn URL.

Example

JSON

  "job_company_linkedin_url": "linkedin.com/company/peopledatalabs"

`job_company_location_address_line_2`


Data Type	`String`
Description	The person’s current company’s headquarters’ street address line 2.

Example

JSON

  "job_company_location_address_line_2": "suite 1670"

`job_company_location_continent`


Data Type	`Enum (String)`
Description	The person’s current company’s headquarters’ continent.

Example

JSON

  "job_company_location_continent": "north america"

`job_company_location_country`


Data Type	`Enum (String)`
Description	The person’s current company’s headquarters’ country.

Example

JSON

  "job_company_location_country": "united states"

`job_company_location_geo`


Data Type	`String`
Description	The person’s current company’s headquarters’ city-center geographic coordinates.

Example

JSON

  "job_company_location_geo": "37.77,-122.41"

`job_company_location_locality`


Data Type	`String`
Description	The person’s current company’s headquarters’ locality, such as a city, town, or other local place name. See common location fields.

Field Details

The person’s current company’s headquarters’ locality, typically the city or local place name for the headquarters address. Examples: san francisco, new york, toronto.

Example

JSON

  "job_company_location_locality": "san francisco"

`job_company_location_metro`


Data Type	`Enum (String)`
Description	The person’s current company’s headquarters’ metro area.

Example

JSON

  "job_company_location_metro": "san francisco, california"

`job_company_location_name`


Data Type	`String`
Description	The person’s current company’s headquarters’ location name.

Example

JSON

  "job_company_location_name": "san francisco, california, united states"

`job_company_location_postal_code`


Data Type	`String`
Description	The person’s current company’s headquarters’ postal code.

Example

JSON

  "job_company_location_postal_code": "94105"

`job_company_location_region`


Data Type	`String`
Description	The person’s current company’s headquarters’ administrative region, such as a U.S. state, Canadian province, or other sub-country division. See Region definition.

Example

JSON

  "job_company_location_region": "california"

`job_company_location_street_address`


Data Type	`String`
Description	The person’s current company’s headquarters’ street address.

Example

JSON

  "job_company_location_street_address": "455 market st"

`job_company_name`


Data Type	`String`
Description	The person’s current company’s name.

Example

JSON

  "job_company_name": "people data labs"

`job_company_size`


Data Type	`Enum (String)`
Description	The person’s current company’s size range.

Example

JSON

  "job_company_size": "51-200"

`job_company_ticker`


Data Type	`String`
Description	The person’s current company’s ticker.

Example

JSON

  "job_company_ticker": "goog"

`job_company_total_funding_raised`


Data Type	`Integer (> 0)`
Description	The cumulative amount of money raised in USD by the person’s current company during all publicly disclosed funding rounds.

Example

JSON

  "job_company_total_funding_raised": 55250000.0

`job_company_twitter_url`


Data Type	`String`
Description	The person’s current company’s Twitter URL.

Example

JSON

  "job_company_twitter_url": "twitter.com/peopledatalabs"

`job_company_type`


Data Type	`Enum (String)`
Description	The person’s current company’s type.

Example

JSON

  "job_company_type": "public"

`job_company_website`


Data Type	`String`
Description	The person’s current company’s website.

Example

JSON

  "job_company_website": "peopledatalabs.com"

Current Job

These fields describe the person’s most recent work experience.

`inferred_salary`


Data Type	`Enum (String)`
Description	The inferred salary range (USD) for the person’s current job.

Field Details

Must be one of our Canonical Inferred Salary Ranges.

Example

JSON

  "inferred_salary": "70,000-85,000"

`job_last_changed`


Data Type	`String (Date)`
Description	The timestamp that reflects when the top-level job information changed.

Field Details

An update is the time when the current employment information is modified in the record.

Limitations of Observed DataThis field reflects observed data. This means that this timestamp will reflect the date when updates were propagated into our data build from our data sources, and may contain some lag time compared to real-life events. For example, if User A changed their job on October 1, 2023, but did not update that publicly until December 1, 2023, our timestamp for job_last_changed will be December.

Example

JSON

  "job_last_changed": "2023-12-01"

`job_last_verified`


Data Type	`String (Date)`
Description	The timestamp that reflects when the top level job information was last validated by a data source.

Field Details

An update is the time when the information in a record is validated through a data source. For more information how this timestamp is generated see: Experience & Location Updates

Example

JSON

  "job_last_verified": "2024-01-05"

`job_onet_code`


Data Type	`String`
Description	The 8-digit O*NET code for the person’s current job title.

Field Details

The 8-digit O*NET code for the person’s current job title, following the current Standard Occupational Classification guidelines. For more details, see the O*NET Field Overview.

Example

JSON

  "job_onet_code": "11-1011.03"

`job_onet_major_group`


Data Type	`String`
Description	The O*NET Major Group associated with the person’s current job title.

Example

JSON

  "job_onet_major_group": "Management Occupations"

`job_onet_minor_group`


Data Type	`String`
Description	The O*NET Minor Group associated with the person’s current job title.

Example

JSON

  "job_onet_minor_group": "Top Executives"

`job_onet_broad_occupation`


Data Type	`String`
Description	The O*NET Broad Occupation associated with the person’s current job title.

Example

JSON

  "job_onet_broad_occupation": "Chief Executives"

`job_onet_specific_occupation`


Data Type	`String`
Description	The O*NET Specific Occupation associated with the person’s current job title.

Example

JSON

  "job_onet_specific_occupation": "Chief Executives"

`job_onet_specific_occupation_detail`


Data Type	`String`
Description	The O*NET Specific Occupation Detail associated with the person’s currentjob title.

Field Details

This field represents a more detailed job title for records where the specific occupation within O*NET’s standard hierarchy isn’t granular enough to accurately describe the job title. For example, the highest level of granularity in O*NET for C-suite positions is Chief Executives. With this field, we can specify the type of executive role. For more details, see the O*NET Field Overview.

Example

JSON

  "job_onet_specific_occupation_detail": "Chief Technology Officer"

`job_start_date`


Data Type	`String (Date)`
Description	The date the person started their current job.

Example

JSON

  "job_start_date": "2015-03"

`job_summary`


Data Type	`String`
Description	User-inputted summary of their current job.

Field Details

The summary is lowercased, but otherwise kept as-is from the raw source.

Example

JSON

  "job_summary": "worked on the \"search analytics\" team to understand our users better"

`job_title`


Data Type	`String`
Description	The person’s current job title.

Field Details

The person’s current job title.

Example

JSON

  "job_title": "co-founder and chief executive officer"

`job_title_class`


Data Type	`Enum (String)`
Description	The expense line item category this employee would fall into.

Field Details

Each class in the list will be one of our Canonical Job Title Classes.

Example

JSON

  "job_title_class": "research_and_development"

`job_title_levels`


Data Type	`Array [Enum (String)]`
Description	The derived level(s) of the person’s current job title.

Field Details

Each level in the list will be one of our Canonical Job Title Levels.

Job Title Levels Hierarchy from “least important” to “most important”:

UnpaidTrainingEntryManager>SeniorPartnerDirectorVPOwnerCXO

Note: The cxo level is a catch-all for “Chief __ Officer” roles, so a CEO, CIO, CTO, etc. will all have job_title_levels: ["cxo"].

Example

JSON

  "job_title_levels": ["cxo", "owner"]

`job_title_role`


Data Type	`Enum (String)`
Description	The derived role of the person’s current job title.

Field Details

The value will be one of our Canonical Job Roles.

Major Update as of v29.1 (February 29.1)In v29.1 (February 2024) we made significant improvements to our role and sub_role categorizations and updated many of the canonical values associated with these fields.Please see our February 2025 Release Notes (v29.1)for further information.

Example

JSON

  "job_title_role": "operations"

`job_title_sub_role`


Data Type	`Enum (String)`
Description	The derived subrole of the person’s current job title.

Field Details

The value will be one of our Canonical Job Sub Roles. Each subrole maps to a role. See Mapping Job Title Class to Roles to Subroles for the complete list.

Example

JSON

  "job_title_sub_role": "logistics"

Demographics

`birth_date`


Data Type	`String (Date)`
Description	The day the person was born.

Field Details

If this field exists, birth_year will agree with it.

Example

JSON

  "birth_date": "1990-12-02"

`birth_year`


Data Type	`Integer`
Description	The year the person was born.

Field Details

The approximated birth year associated with this person profile. If a profile has a birth_date, the birth_year field will match it.

Example

JSON

  "birth_year": 1990

`sex`

gender was renamed to sex in v26.0In v26.0 (April 2024) we renamed this field from gender to sex, in accordance with legislative changes defining aspects of gender as sensitive personal data (which PDL does not process or output).Please see our April 2024 Release Announcement (v26.0) for further information.


Data Type	`Enum (String)`
Description	The person’s sex.

Field Details

The value will always be one of our Canonical Sex.

Example

JSON

  "sex": "male"

`languages`


Data Type	`Array [Object]`
Description	Languages the person knows.

Field Details

The languages listed are based on user input, we do not verify them.

Field	Data Type	Description
`name`	`Enum (String)`	The language. Must be one of our Canonical Languages.
`proficiency`	`Integer (1-5)`	Self-ranked language proficiency from 1 (limited) to 5 (fluent).

Sort OrderLanguages are sorted by proficiency first, followed by name, all in reverse order (highest proficiency first, Z→A):

proficiency (highest first)
name (Z→A)

Example

JSON

  "languages": [
    {
      "name": "english",
      "proficiency": 5
    }
  ]

Education

`education`


Data Type	`Array [Object]`
Description	The person’s education information.

Field Details

The education objects associated with this person profile, which, when output in CSV format, have indexing based on recency and associativity. Each education object in the list will include the following data:

Field	Data Type	Description
`degrees`	`Array [Enum (String)]`	The degrees the person earned at the school. All values will be Canonical Education Degrees
`end_date`	`String (Date)`	The date the person left the school. If the person is still at the school, will be `null`.
`gpa`	`Float`	The GPA the person earned at the school.
`majors`	`Array [Enum (String)]`	All majors earned at the school. All values will be Canonical Education Majors.
`minors`	`Array [Enum (String)]`	All minors earned at the school. All values will be Canonical Education Majors.
`raw`	`Array [String]`	Raw education data that was parsed into the `degrees`, `majors`, and `minors` fields.
`school`	`Object`	The school the person attended.
`start_date`	`String (Date)`	The date the person started at the school.
`summary`	`String`	User-inputted summary of their education.

Sort OrderEducation entries are sorted first by start_date, then by end_date. If dates are identical, then sorting occurs by school.name, followed by majors, minors and degrees, all in reverse order (most recent first, Z→A):

start_date(most recent first)
end_date (most recent first)
school.name (Z→A)
countriesmajors (Z→A)
minors (Z→A)
degrees (Z→A)

`education.school`

To tap into our school matching logic, use our School Cleaner API to retrieve possible school values.

Field	Sub Field	Data Type	Description
`domain`		`String`	The primary website domain associated with the school.
`facebook_url`		`String`	The school’s Facebook URL
`id`		`String`	The NON-PERSISTENT ID for the school in our records.
`linkedin_id`		`String`	The school’s LinkedIn ID
`linkedin_url`		`String`	The school’s LinkedIn URL
`location`		`Object`	The location of the school. See Common Location Fields for detailed field descriptions.
	`continent`	`Enum (String)`
	`country`	`Enum (String)`
	`locality`	`String`
	`name`	`String`
	`region`	`String`
`name`		`String`	The name of the school.
`raw`		`Array [String]`	Raw school name.
`twitter_url`		`String`	The school’s Twitter URL
`type`		`Enum (String)`	The school type. Will be one of our Canonical School Types.
`website`		`String`	The website URL associated with the school, which could include subdomains.

Example

JSON

  "education": [
    {
      "school": {
        "name": "university of oregon",
        "type": "post-secondary institution",
        "id": "64LkgfdwWYkCC2TjbldMDQ_0",
        "location": {
          "name": "eugene, oregon, united states",
          "locality": "eugene",
          "region": "oregon",
          "country": "united states",
          "continent": "north america"
        },
        "linkedin_url": "linkedin.com/school/university-of-oregon",
        "linkedin_id": "19207",
        "facebook_url": "facebook.com/universityoforegon",
        "twitter_url": "twitter.com/uoregon",
        "website": "uoregon.edu",
        "domain": "uoregon.edu",
        "raw": [
          "university of oregon"
        ]
      },
      "end_date": "2014",
      "start_date": "2010",
      "gpa": null,
      "degrees": [],
      "majors": [
        "entrepreneurship"
      ],
      "minors": [],
      "raw": [
        "data analytics & entrepreneurship",
        ", entrepreneurship",
        "entrepreneurship"
      ],
      "summary": "when i was at oregon i volunteered at a local homeless shelter 3 days a week"
    },
  ]

Location

For more information on our standard location fields, see Common Location Fields.

`countries`


Data Type	`Array [Enum (String)]`
Description	All countries associated with the person.

Sort OrderCountries are sorted using location sort order, with any duplicate countries removed.Location Sort OrderLocations are sorted by primary location first. The remaining locations are sorted by first_seen, last_seen, location_name, street_address, then address_line_2 in descending order (most recent first, Z→A):

Primary location first
Sort by first_seen (most recent first)
Sort by last_seen (most recent first)
Sort by location_name (Z→A)
Sort by street_address (Z→A)
Sort by address_line_2 (Z→A)

Example

JSON

  "countries": [
    "united states"
  ]

`location_address_line_2`


Data Type	`String`
Description	The person’s current street address line 2.

Example

JSON

  "location_address_line_2": "apartment 12"

`location_continent`


Data Type	`Enum (String)`
Description	The continent of the person’s current address. One of our Canonical Continents.

Example

JSON

  "location_continent": "north america"

`location_country`


Data Type	`Enum (String)`
Description	The country of the person’s current address. One of our Canonical Countries.

Example

JSON

  "location_country": "united states"

`location_geo`


Data Type	`String`
Description	The geo code of the city center of the person’s current address.

Example

JSON

  "location_geo": "37.87,-122.27"

`location_last_updated`


Data Type	`String (Date)`
Description	The timestamp that a new data source contributed to the record for the person’s current address.

Field Details

An update is the time when either new information is added to the record or existing information is validated.

Example

JSON

  "location_last_updated": "2018-11-05"

`location_locality`


Data Type	`String`
Description	The locality of the person’s current address, usually a city, town, neighborhood, or other local place name.

Field Details

The locality for the person’s current address, typically the city or local place name component of the address. Examples: berkeley, boston, cambridge.

Example

JSON

  "location_locality": "berkeley"

`location_metro`


Data Type	`Enum (String)`
Description	The metro of the person’s current address. One of our Canonical Metros.

Example

JSON

  "location_metro": "san francisco, california"

`location_name`


Data Type	`String`
Description	The location of the person’s current address.

Example

JSON

  "location_name": "berkeley, california, united states"

`location_names`


Data Type	`Array [String]`
Description	All location names associated with the person.

Sort OrderLocation names are sorted by location order with duplicate names removedLocation Sort OrderLocations are sorted by primary location first. The remaining locations are sorted by first_seen, last_seen, location_name, street_address, then address_line_2 in descending order (most recent first, Z→A):

Primary location first
Sort by first_seen (most recent first)
Sort by last_seen (most recent first)
Sort by location_name (Z→A)
Sort by street_address (Z→A)
Sort by address_line_2 (Z→A)

Example

JSON

  "location_names": [
    "berkeley, california, united states",
    "san francisco, california, united states"
  ]

`location_postal_code`


Data Type	`String`
Description	The postal code of the person’s current address.

Example

JSON

  "location_postal_code": "94704"

`location_region`


Data Type	`String`
Description	The administrative region of the person’s current address, such as a U.S. state, Canadian province, or other sub-country division. See Region definition.

Example

JSON

  "location_region": "california"

`location_street_address`


Data Type	`String`
Description	The person’s current street address.

Example

JSON

  "location_street_address": "455 fake st"

`regions`


Data Type	`Array [String]`
Description	All regions associated with the person.

Sort OrderRegions are sorted by location order, with any duplicate regions removed.Location Sort OrderLocations are sorted by primary location first. The remaining locations are sorted by first_seen, last_seen, location_name, street_address, then address_line_2 in descending order (most recent first, Z→A):

Primary location first
Sort by first_seen (most recent first)
Sort by last_seen (most recent first)
Sort by location_name (Z→A)
Sort by street_address (Z→A)
Sort by address_line_2 (Z→A)

Example

JSON

  "regions": [
    "california, united states"
  ]

`street_addresses`


Data Type	`Array [Object]`
Description	All street addresses associated with the person.

Field Details

Each address associated with the person will be added to this list as its own object. In addition to the Common Location Fields, street_addresses will also include:

Field	Data Type	Description
`first_seen`	`String (Date)`	The date that this entity was first associated with the Person record.
`last_seen`	`String (Date)`	The date that this entity was last associated with the Person record.
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this entity with the Person record.

Sort OrderStreet addresses are sorted by location order.Location Sort OrderLocations are sorted by primary location first. The remaining locations are sorted by first_seen, last_seen, location_name, street_address, then address_line_2 in descending order (most recent first, Z→A):

Primary location first
Sort by first_seen (most recent first)
Sort by last_seen (most recent first)
Sort by location_name (Z→A)
Sort by street_address (Z→A)
Sort by address_line_2 (Z→A)

Example

JSON

  "street_addresses": [
    {
      "name": "berkeley, california, united states",
      "locality": "berkeley",
      "metro": "san francisco, california",
      "region": "california",
      "country": "united states",
      "continent": "north america",
      "street_address": "455 fake st",
      "address_line_2": "apartment 12",
      "postal_code": "94704",
      "geo": "37.87,-122.27",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    }
  ]

Lower Confidence Data

PDL values high confidence data that is very likely to be associated with a person. The data in these fields have lower confidence than the data used in other fields.

`possible_birth_dates`


Data Type	`Array [String (Date)]`
Description	Birthdays associated with this person that have a lower level of confidence.

Field Details

The dates in this field use the same format as the birth_date field.

Sort OrderPossible birth dates are sorted by num_sources, last_seen, first_seen, birth_date, all in reverse order (highest first, most recent first):

num_sources (highest first)
last_seen (most recent first)
first_seen (most recent first)
birth_date (most recent first)

Example

JSON

  "possible_birth_dates": [
    "1991-05-26",
    "1992-05-26"
  ]

`possible_emails`


Data Type	`Array [Object]`
Description	Email addresses associated with this person that have a lower level of confidence.

Field Details

This field uses the same format as the emails field.

Sort OrderPossible emails are sorted by last_seen, first_seen, email, all in reverse order (most recent first, Z→A):

last_seen (most recent first)
first_seen (most recent first)
email (Z→A)

Example

JSON

  "possible_emails": [
    {
      "address": "sean@peopledatalabs.com",
      "type": null,
      "first_seen": "2021-06-13",
      "last_seen": "2024-09-28",
      "num_sources": 2
    }
  ]

`possible_location_names`


Data Type	`Array [String]`
Description	Locations associated with this person that have a lower level of confidence.

Field Details

This field uses the same format as the location_names field. Possible locations are inferred based on phone area codes, university location, and other associations.

Sort OrderPossible locations are sorted by first_seen, last_seen, location.name, all in reverse order (most recent first, Z→A):

first_seen (most recent first)
last_seen (most recent first)
location.name (Z→A)

Example

JSON

  "possible_location_names": [
    "berkeley, california, united states",
    "san francisco, california, united states"
  ]

`possible_phones`


Data Type	`Array [Object]`
Description	Phone numbers associated with this person that have a lower level of confidence.

Field Details

This field uses the same format as the phones field.

Sort OrderPossible phones are sorted by num_sources, last_seen, first_seen, all in reverse order and using the E.164 format (highest first, most recent first):

num_sources (highest first)
last_seen (most recent first)
first_seen(most recent first)

Example

JSON

  "possible_phones": [
    {
      "number": "+15558675309",
      "first_seen": "2021-06-13",
      "last_seen": "2024-09-28",
      "num_sources": 2
    }
  ]

`possible_profiles`


Data Type	`Array [Object]`
Description	Social profiles associated with this person that have a lower level of confidence.

Field Details

This field uses the same format as the profiles field.

Sort OrderPossible profiles are sorted first by return status codes (200 > unknown > 404). The array is then sorted by number of profiles globally, last_seen, first_seen, username, id, all in reverse order (highest first, most recent first, Z→A):

Status Codes:
- Profiles with 200 return status codes are first
- Profiles with unknown return status codes come next
- Profiles with 404 return status codes are placed last
Number of profiles globally (highest first)
last_seen (most recent first)
first_seen (most recent first)
username (Z→A)
id (Z→A)

Example

JSON

  "possible_profiles": [
    {
      "network": "linkedin",
      "id": "145991517",
      "url": "linkedin.com/in/seanthorne",
      "username": "seanthorne",
      "first_seen": "2021-06-13",
      "last_seen": "2024-09-28",
      "num_sources": 2
    }
  ]

`possible_street_addresses`


Data Type	`Array [Object]`
Description	Addresses associated with this person that have a lower level of confidence.

Field Details

This field uses the same format as the street_addresses field.

Sort OrderPossible street addresses are sorted by first_seen, last_seen, location.name, location.street_address, location.address_line_2, all in reverse order (most recent first, Z→A):

first_seen (most recent first)
last_seen (most recent first)
location.name (Z→A)
location.street_address (Z→A)
location.address_line_2 (Z→A)

Example

JSON

  "possible_street_addresses": [
    {
      "name": "berkeley, california, united states",
      "locality": "berkeley",
      "metro": "san francisco, california",
      "region": "california",
      "country": "united states",
      "continent": "north america",
      "street_address": "455 fake st",
      "address_line_2": "apartment 12",
      "postal_code": "94704",
      "geo": "37.87,-122.27",
      "first_seen": "2021-06-13",
      "last_seen": "2024-09-28",
      "num_sources": 2
    }
  ]

We currently cover person social profiles on our Canonical Profile Networks. All profiles we’ve found for a person will be added to the profiles list. Each social profile URL has one or more standard formats that we parse and turn into a standard PDL format for that social URL. We invalidate profiles that have non-valid person stubs (for example, linkedin.com/company), and we also have a blacklist of usernames that we know are invalid. We do not validate if a URL is valid (that is, whether you can access it) because doing this at scale is considered a Direct Denial of Service (DDoS) attack and/or a form of crawling. This is highly discouraged! We try to mitigate invalid URLs as much as possible by using Entity Resolution (Merging) to link URLs together and then tagging the primary URL at the top level for key networks.

`facebook_friends`


Data Type	`Integer (>= 0)`
Description	The number of Facebook friends the person has.

Example

JSON

  "facebook_friends": 3912

`facebook_id`


Data Type	`String`
Description	The person’s Facebook profile ID based on source agreement.

Example

JSON

  "facebook_id": "1089351304"

`facebook_url`


Data Type	`String`
Description	The person’s Facebook profile URL based on source agreement.

Example

JSON

  "facebook_url": "facebook.com/deseanthorne"

`facebook_username`


Data Type	`String`
Description	The person’s Facebook profile username based on source agreement.

Example

JSON

  "facebook_username": "deseanthorne"

`github_url`


Data Type	`String`
Description	The person’s GitHub profile URL based on source agreement.

Example

JSON

  "github_url": "github.com/deseanathan_thornolotheu"

`github_username`


Data Type	`String`
Description	The person’s GitHub profile username based on source agreement.

Example

JSON

  "github_username": "deseanathan_thornolotheu"

`linkedin_connections`


Data Type	`Integer (>= 0)`
Description	The number of LinkedIn connections the person has.

Field Details

Typically between 0-500.

Example

JSON

  "linkedin_connections": 432

`linkedin_id`


Data Type	`String`
Description	The person’s LinkedIn profile ID. This is null when no values in the “profiles” array are active.

Example

JSON

  "linkedin_id": "145991517"

`linkedin_url`


Data Type	`String`
Description	The person’s current LinkedIn profile URL. This is null when no values in the “profiles” array are active.

Example

JSON

  "linkedin_url": "linkedin.com/in/seanthorne"

`linkedin_username`


Data Type	`String`
Description	The person’s LinkedIn profile username. This is null when no values in the “profiles” array are active.

Example

JSON

  "linkedin_username": "seanthorne"

`profiles`


Data Type	`Array [Object]`
Description	Social profiles associated with the person.

Field Details

Each profile associated with the person will be added to this list as its own object.

Field	Data Type	Description
`id`	`String`	The profile ID (format varies based on social network).
`first_seen`	`String (Date)`	The date that this entity was first associated with the Person record.
`last_seen`	`String (Date)`	The date that this entity was last associated with the Person record.
`network`	`Enum (String)`	The social network the profile is on. Must be one of our Canonical Profile Networks.
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this entity with the Person record.
`url`	`String`	The profile URL.
`username`	`String`	The profile username.

Sort OrderProfiles are sorted with the primary profiles listed first (facebook, linkedin, twitter, and github, in that order). The rest of the array is then sorted by status codes (200 > unknown > 404), number of profiles globally, last_seen, first_seen, username, id, all in reverse order (highest first, most recent first, Z→A):

Primary profiles first
1. Facebook
2. LinkedIn
3. Twitter
4. Github
Status Codes:
- Profiles with 200 return status codes are first
- Profiles with unknown return status codes come next
- Profiles with 404 return status codes are placed last
Number of profiles globally (highest first)
last_seen (most recent first)
first_seen (most recent first)
username (Z→A)
id (Z→A)

Example

JSON

  "profiles": [
    {
      "network": "linkedin",
      "id": "145991517",
      "url": "linkedin.com/in/seanthorne",
      "username": "seanthorne",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    }
  ]

`twitter_url`


Data Type	`String`
Description	The person’s Twitter profile URL based on source agreement.

Example

JSON

  "twitter_url": "twitter.com/seanthorne5"

`twitter_username`


Data Type	`String`
Description	The person’s Twitter profile username based on source agreement.

Example

JSON

  "twitter_username": "seanthorne5"

PDLScores™

PDLScores™ are generated scoring fields that help evaluate and prioritize Person profiles. For GA availability, bundle information, example responses, and common workflows, see PDLScores™ for Person Data.

Resume Slice OnlyPDLScores™ and their associated score factors are only available on records in the Resume Slice of the Person dataset. Profiles outside the Resume Slice return null for these fields.

`profile_score`


Data Type	`String`
Description	A PDL-generated signal indicating how likely a profile is to represent a real person.

This field is only available on records in the Resume Slice. Profiles outside the Resume Slice return null.

For GA availability, bundle information, and example responses, see PDLScores™ for Person Data.

Field Details

The profile_score helps identify profiles that appear to represent real people. PDL evaluates signals such as profile completeness, profile age, LinkedIn URL validity, and connection count. This field returns one of the following values:

Value	Meaning
`positive signals`	PDL has strong indicators that the profile represents a real person
`neutral signals`	PDL has mixed or inconclusive indicators
`negative signals`	PDL has indicators that the profile may be low-quality, fake, or otherwise less reliable
`null`	PDL does not have enough data to evaluate the profile

Example

JSON

  "profile_score": "positive signals"

`profile_score_factors`


Data Type	`Object`
Description	Supporting factors used to generate `profile_score`.

These score factors are only available on records in the Resume Slice. Profiles outside the Resume Slice return null.

For GA availability, bundle information, and example responses, see PDLScores™ for Person Data.

Field Details

The profile_score_factors object provides additional context for customers who want to build custom filtering, ranking, or review logic around profile_score. These factors are available through the premium PDL Score Factors bundle. PDL does not expose the raw score used to assign the final profile_score bucket.

Field	Data Type	Description
`attribute_fill_rate`	`Float`	The share of selected resume attributes present on the profile
`profile_age_months`	`Integer`	The age of the profile in months, based on PDL’s first observation of the trusted LinkedIn profile
`has_valid_url`	`Float`	A signal indicating whether the profile has a valid LinkedIn URL
`meets_connection_threshold`	`Integer`	A signal indicating whether the profile meets PDL’s LinkedIn connection threshold

Example

JSON

  "profile_score_factors": {
    "attribute_fill_rate": 0.625,
    "profile_age_months": 110,
    "has_valid_url": 1,
    "meets_connection_threshold": 1
  }

`profile_score_factors.attribute_fill_rate`


Data Type	`Float`
Description	The share of selected resume attributes present on the profile.

Field Details

This factor measures how filled out the profile is across selected resume attributes, such as education, experience, headline, summary, skills, interests, certifications, and custom LinkedIn slug. Higher values indicate that more selected attributes are present on the profile.

Example

JSON

  "attribute_fill_rate": 0.625

`profile_score_factors.profile_age_months`


Data Type	`Integer`
Description	The age of the profile in months, based on PDL’s first observation of the trusted LinkedIn profile.

Field Details

This factor measures the number of months since PDL first observed the profile. Older profiles generally provide a stronger historical signal that the profile represents a legitimate professional identity.

Example

JSON

  "profile_age_months": 110

`profile_score_factors.has_valid_url`


Data Type	`Float`
Description	A signal indicating whether the profile has a valid LinkedIn URL.

Field Details

This factor evaluates the LinkedIn URL associated with the profile.

Value	Meaning
`1`	PDL has detected a valid LinkedIn URL
`0.5`	LinkedIn URL validity is unknown
`0`	PDL has detected an invalid LinkedIn URL

Example

JSON

  "has_valid_url": 1

`profile_score_factors.meets_connection_threshold`


Data Type	`Integer`
Description	A signal indicating whether the profile meets PDL’s LinkedIn connection threshold.

Field Details

This factor evaluates whether the profile meets PDL’s LinkedIn connection threshold.

Value	Meaning
`1`	The profile meets the connection threshold
`0`	The profile does not meet the threshold

Example

JSON

  "meets_connection_threshold": 1

`activity_score`


Data Type	`String`
Description	A PDL-generated signal indicating how likely a profile is to be actively maintained.

This field is only available on records in the Resume Slice. Profiles outside the Resume Slice return null.

For GA availability, bundle information, and example responses, see PDLScores™ for Person Data.

Field Details

The activity_score helps identify profiles that appear to be actively maintained. PDL evaluates signals such as recent resume activity, connection count changes, and user-edited profile changes. This field returns one of the following values:

Value	Meaning
`positive signals`	PDL has strong indicators that the profile is actively maintained
`neutral signals`	PDL has mixed or inconclusive activity indicators
`negative signals`	PDL has indicators that the profile may be inactive or abandoned
`null`	PDL does not have enough data to evaluate activity

Example

JSON

  "activity_score": "neutral signals"

`activity_score_factors`


Data Type	`Object`
Description	Supporting factors used to generate `activity_score`.

These score factors are only available on records in the Resume Slice. Profiles outside the Resume Slice return null.

For GA availability, bundle information, and example responses, see PDLScores™ for Person Data.

Field Details

The activity_score_factors object provides additional context for customers who want to build custom activity, routing, prioritization, or suppression logic around activity_score. These factors are available through the premium PDL Score Factors bundle. PDL does not expose the raw score used to assign the final activity_score bucket.

Field	Data Type	Description
`connection_change`	`Float`	A signal indicating whether PDL has observed changes in the profile’s LinkedIn connection count
`profile_change`	`Integer`	A signal indicating whether PDL has observed user-edited profile changes recently
`months_since_last_end_resume`	`Integer`	The number of months since the profile’s latest resume end date, when no active experience or education is present

Example

JSON

  "activity_score_factors": {
    "connection_change": 0.5,
    "profile_change": 1,
    "months_since_last_end_resume": 0
  }

`activity_score_factors.connection_change`


Data Type	`Float`
Description	A signal indicating whether PDL has observed changes in the profile’s LinkedIn connection count.

Field Details

This factor evaluates whether PDL has observed a change in the profile’s LinkedIn connection count across recent releases.

Value	Meaning
`1`	PDL has observed a connection count change in the last 2 years
`0.5`	PDL has observed a connection count change in the last 5 years, but not in the last 2 years
`0`	PDL has not observed a connection count change

Example

JSON

  "connection_change": 0.5

`activity_score_factors.profile_change`


Data Type	`Integer`
Description	A signal indicating whether PDL has observed user-edited profile changes recently.

Field Details

This factor evaluates whether PDL has observed an update to user-edited profile fields such as headline, summary, experience, education, location, certifications, skills, or interests.

Value	Meaning
`1`	PDL has observed a recent user-edited profile change
`0`	PDL has not observed a recent user-edited change

Example

JSON

  "profile_change": 1

`activity_score_factors.months_since_last_end_resume`


Data Type	`Integer`
Description	The number of months since the profile’s latest resume end date, when no active experience or education is present.

Field Details

This factor evaluates resume recency by looking at experience and education activity. If the profile has an active job or active education, this field is null. Otherwise, it reflects the number of months since the latest end date on the profile’s resume data. Lower values generally indicate more recent resume activity.

Example

JSON

  "months_since_last_end_resume": 0

Work History

`certifications`


Data Type	`Array [Object]`
Description	Any certifications the person has.

Field Details

The certifications listed are based on user input, we do not verify them.

Field	Data Type	Description
`end_date`	`String (Date)`	The expiration date of the certification.
`name`	`String`	Certification name
`organization`	`String`	The organization awarding the certification.
`start_date`	`String (Date)`	The date the certification was awarded.

Sort OrderCertifications are sorted first by start_date, then by end_date and finally by name, all in reverse order (most recent first, Z→A):

start_date (most recent first)
end_date (most recent first)
name (Z→A)

Example

JSON

  "certifications": [
    {
      "name": "machine learning certification",
      "organization": "coursera",
      "start_date": "2022-03",
      "end_date": "2023-04"
    }
  ]

`experience`


Data Type	`Array [Object]`
Description	The person’s work experience.

Field Details

The experience object that is tagged as experience.is_primary = True is copied over to the flattened job_ fields (see Current Job and Current Company). Each work experience object contains the following fields:

Field	Data Type	Description
`company`	`Object`	The company where the person worked.
`end_date`	`String (Date)`	The date the person left the company. If the person is still working for the company, will be `null`.
`first_seen`	`String (Date)`	The date that this entity was first associated with the Person record.
`last_seen`	`String (Date)`	The date that this entity was last associated with the Person record.
`is_primary`	`Boolean`	Whether this is the person’s current job or not. If `true`, this experience will be used to populate the `job_` fields.
`location_names`	`Array [String]`	Locations where the person has worked while with this company
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this entity with the Person record.
`start_date`	`String (Date)`	The date the person started at the company.
`summary`	`String`	User-inputted summary of their work experience.
`title`	`Object`	The person’s job title while at the company.

Sort OrderExperience entries are sorted with the primary experience first. The remaining entries are then sorted by start_date, end_date, company.name, and title.name, all in reverse order (most recent first, Z→A):

Primary Experience (is_primary = True )
start_date (most recent first)
end_date (most recent first)
company.name (Z→A)
title.name (Z→A)

`experience.company`

The fields in experience.company map to the corresponding fields in our Company Schema. The same parsing and formatting logic apply.

Field	Sub Field	Data Type	Description
`facebook_url`		`String`	The company’s Facebook URL
`founded`		`Integer (> 0)`	The founding year of the company.
`id`		`String`	The company’s PDL ID
`industry`		`Enum (String)`	The self-identified industry of the company. Must be one of the Canonical Industries.
`industry_v2`		`Enum (String)`	The v2 industry for the company. Industry v2 is the self-reported industry from an expanded list of Canonical v2 Industries. If no industry is found, the field will be `null`
`linkedin_id`		`String`	The company’s LinkedIn ID
`linkedin_url`		`String`	The company’s LinkedIn URL
`location`		`Object`	The location of the company’s headquarters. See Common Location Fields for detailed field descriptions.
	`address_line_2`	`String`
	`continent`	`Enum (String)`
	`country`	`Enum (String)`
	`geo`	`String`
	`locality`	`String`
	`metro`	`Enum (String)`
	`name`	`String`
	`postal_code`	`String`
	`region`	`String`
	`street_address`	`String`
`name`		`String`	The company name, cleaned and standardized.
`raw`		`Array [String]`	Raw company name.
`size`		`Enum (String)`	The self-reported company size range. Must be one of our Canonical Company Sizes.
`ticker`		`String`	The company ticker. This field will only have a value if the company’s `type` is `public`.
`twitter_url`		`String`	The company’s Twitter URL
`type`		`Enum (String)`	The company type. Must be one of our Canonical Company Types.
`website`		`String`	The company’s primary website, cleaned and standardized.

`experience.title`

See the corresponding Current Job fields for more details on the information included and formatting of these fields.

Field	Data Type	Description
`levels`	`Array [Enum (String)]`	Canonical Job Title Levels.
`name`	`String`	The cleaned job title.
`raw`	`Array [String]`	Raw job title input.
`role`	`Enum (String)`	One of the Canonical Job Roles.
`sub_role`	`Enum (String)`	One of the Canonical Job Sub Roles.
`class`	`Enum (String)`	One of the Canonical Job Classes .

Sort Order (experience.title.levels)Title levels are sorted from most important to least important based on the following ranking:

cxo (first)
owner
vp
director
partner
senior
manager
entry
training
unpaid (last)

Example

JSON

  "experience": [
    {
      "company": {
        "name": "people data labs",
        "size": "11-50",
        "id": "peopledatalabs",
        "founded": 2015,
        "industry": "computer software",
        "location": {
          "name": "san francisco, california, united states",
          "locality": "san francisco",
          "region": "california",
          "metro": "san francisco, california",
          "country": "united states",
          "continent": "north america",
          "street_address": "455 market street",
          "address_line_2": "suite 1670",
          "postal_code": "94105",
          "geo": "37.77,-122.41"
        },
        "linkedin_url": "linkedin.com/company/peopledatalabs",
        "linkedin_id": "18170482",
        "facebook_url": "facebook.com/peopledatalabs",
        "twitter_url": "twitter.com/peopledatalabs",
        "website": "peopledatalabs.com",
        "ticker": null,
        "type": "private",
        "raw": [
          "people data labs"
        ],
      },
      "location_names": ["san francisco, california, united states"],
      "end_date": null,
      "start_date": "2015-03",
      "title": {
        "name": "chief executive officer and co-founder",
        "raw": [
          "co-founder &amp; ceo",
          "co-founder & ceo",
          "co-founder and chief executive officer"
        ],
        "role": "operations",
        "sub_role": "executive",
        "class": "general_and_administrative"
        "levels": [
          "cxo",
          "owner"
        ],
      },
      "is_primary": true,
      "summary": "worked on the \"search analytics\" team to understand our users better",
      "first_seen": "2018-10-11",
      "last_seen": "2022-11-15",
      "num_sources": 17
    },
  ]

`headline`


Data Type	`String`
Description	The brief headline associated with a person profile.

Field Details

The self-written headline tied to the person profile (often a LinkedIn headline). The summary is lowercased, but otherwise kept as-is from the raw source.

Example

JSON

  "headline": "senior data engineer at people data labs"

`industry`


Data Type	`Enum (String)`
Description	The most relevant industry for this person based on their work history.

Field Details

A person’s industry is determined based on their tagged personal industries and the industries of the companies that they have worked for. The value will be one of our Canonical Industries.

Example

JSON

  "industry": "computer software"

`inferred_years_experience`


Data Type	`Integer (0 - 100)`
Description	The person’s inferred years of total work experience.

Field Details

The value will be between 0 and 100.

Example

JSON

  "inferred_years_experience": 7

`interests`


Data Type	`Array [String]`
Description	The person’s self-reported interests.

Field Details

Each interest is cleaned (lowercased, stripped of whitespace, etc.). We don’t have a canonical list of interests but we remove profanity and do some basic cleaning.

Sort OrderInterests are sorted alphabetically (from A→Z)

Example

JSON

  "interests": [
    "data",
    "software"
  ]

`job_history`


Data Type	`Array [Object]`
Description	Additional professional positions that may have been removed or changed on resumes.

Field Details

Any additional job history information PDL has that is not included in the experience field. Usually these are positions that have been removed or changed on resumes.

Field	Data Type	Description
`company_id`	`String`	PDL Company ID
`company_name`	`String`	Company Name
`first_seen`	`String (Date)`	The date that this experience was first associated with this record.
`last_seen`	`String (Date)`	The date that this experience was last associated with this record.
`num_sources`	`Integer (> 0)`	The number of sources that have contributed to the association of this profile with this record.
`title`	`String`	Job Title at this company.

Sort OrderJob history entries are sorted by start_date, end_date, company.name, and title.name, all in reverse order (most recent first, Z→A):

start_date (most recent first)
end_date (most recent first)
company.name (Z→A)
title.name (Z→A)

Example

JSON

  "job_history": [
    {
      "company_id": "OMdETRug8CpuRDWGkhQ35wx8CvVk",
      "company_name": "auntie annes",
      "title": "food service supervisor",
      "first_seen": "2016-05-17",
      "last_seen": "2020-05-30",
      "num_sources": 12
    }
  ]

`skills`


Data Type	`Array [String]`
Description	The person’s self-reported skills.

Field Details

Each skill is cleaned (lowercased, stripped of whitespace, etc.). We do not always strip punctuation because it can be relevant for some skills (ex: "c++" vs "c"). We do not do any canonicalization, so "java" and "java 8.0" are considered separate skills. For this reason, we encourage our customers to use fuzzy text matching with the skills field.

Sort OrderSkills are sorted alphabetically (from A→Z)

Example

JSON

  "skills": [
    "entrepreneurship"
  ]

`summary`


Data Type	`String`
Description	User-inputted personal summary.

Field Details

The self-written summary tied to the person profile (often a LinkedIn summary). The summary is lowercased, but otherwise kept as-is from the raw source.

Example

JSON

  "summary": "growth-hacker and digital nomad"

PDL Record Information & Metadata

`dataset_version`


Data Type	`String`
Description	The major or minor release number.

Field Details

This field currently exists in Person Enrichment API responses. Note: This number corresponds to the data release number, not the API release number.

Example

JSON

  "dataset_version": "19.2"

`first_seen`


Data Type	`String (Date)`
Description	The date when this record was first created in our data.

Example

JSON

  "first_seen": "2017-06-02"

`num_records`


Data Type	`Integer (> 0)`
Description	The number of unique raw records contributing to this specific PDL profile.

Example

JSON

  "num_records": 420

`num_sources`


Data Type	`Integer (> 0)`
Description	The number of unique sources contributing to this specific PDL profile.

Example

JSON

  "num_sources": 72

operation_id


Data Type	`String`
Description	An identifier for an operation in a Data License delivery, used for troubleshooting.

Field Details

This field exists only in Data License deliveries, and allows PDL employees to identify the timestamp and operations performed on the internal data in order to return a record in a delivery.

Example

JSON

  "operation_id": "acee3bde2e1a2cb7e75c57b80d5b7bc2d5de5b02e7ea51f91304c28df77251dc"

Person Data Field Bundles

Elasticsearch Mapping - Person

⌘I

​Overview

​Identifiers

​first_name

​Field Details

​Example

​full_name

​Field Details

​Example

​id

​Field Details

​Example

​last_initial

​Field Details

​Example

​last_name

​Field Details

​Example

​middle_initial

​Field Details

​Example

​middle_name

​Field Details

​Example

​name_aliases

​Field Details

​Example

​Contact Information

​emails

​Field Details

​Example

​mobile_phone

​Field Details

​Example

​personal_emails

​Field Details

​Example

​phone_numbers

​Field Details

​Example

​phones

​Field Details

​Example

​recommended_personal_email

​Field Details

​Example

​work_email

​Field Details

​Example

​Current Company

​job_company_12mo_employee_growth_rate

​Example

​job_company_facebook_url

​Example

​job_company_founded

​Example

​job_company_employee_count

​Example

​job_company_id

​Example

​job_company_industry

​Example

​job_company_industry_v2

​Example

​Details

​job_company_inferred_revenue

​Example

​job_company_linkedin_id

​Example

​job_company_linkedin_url

​Example

​job_company_location_address_line_2

​Example

​job_company_location_continent

​Example

​job_company_location_country

​Example

​job_company_location_geo

​Example

​job_company_location_locality

​Field Details

Overview

Identifiers

`first_name`

Field Details

Example

`full_name`

Field Details

Example

`id`

Field Details

Example

`last_initial`

Field Details

Example

`last_name`

Field Details

Example

`middle_initial`

Field Details

Example

`middle_name`

Field Details

Example

`name_aliases`

Field Details

Example

Contact Information

`emails`

Field Details

Example

`mobile_phone`

Field Details

Example

`personal_emails`

Field Details

Example

`phone_numbers`

Field Details

Example

`phones`

Field Details

Example

`recommended_personal_email`

Field Details

Example

`work_email`

Field Details

Example

Current Company

`job_company_12mo_employee_growth_rate`

Example

`job_company_facebook_url`

Example

`job_company_founded`

Example

`job_company_employee_count`

Example

`job_company_id`

Example

`job_company_industry`

Example

`job_company_industry_v2`

Example

Details

`job_company_inferred_revenue`

Example

`job_company_linkedin_id`

Example

`job_company_linkedin_url`

Example

`job_company_location_address_line_2`

Example

`job_company_location_continent`

Example

`job_company_location_country`

Example

`job_company_location_geo`

Example

`job_company_location_locality`

Field Details