Overview

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

📘
Field Availability
Not 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`

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

Field Details

The person's first name.

Example

  "first_name": "sean"

`full_name`

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

Field Details

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

Example

  "full_name": "sean thorne"

`id`

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

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 https://docs.peopledatalabs.com/docs/persistent-ids for more information.

Example

  "id": "qEnOZ5Oh0poWnQ1luFBfVw_0000"

`last_initial`

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

Field Details

The first letter of the person's last name.

Example

  "last_initial": "t"

`last_name`

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

Field Details

The person's last name.

Example

  "last_name": "thorne"

`middle_initial`

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

Field Details

The first letter of the person's middle name.

Example

  "middle_initial": "f"

`middle_name`

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

Field Details

The person's middle name.

Example

  "middle_name": "fong"

`name_aliases`

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

Field Details

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

Example

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

Contact Information

`emails`

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

Field Details

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.
`type`	`Enum (String)`	The type of email address. Must be one of our Canonical Email Types

Example

  "emails": [
    {
      "address": "[email protected]",
      "type": "current_professional",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    },
    {
      "address": "[email protected]",
      "type": "personal",
      "first_seen": "2017-06-02",
      "last_seen": "2019-07-18",
      "num_sources": 17
    }
  ]

`mobile_phone`

Description	The person's mobile phone number.
Data Type	`String (Phone)`

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

  "mobile_phone": "+15558675309"

`personal_emails`

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

Field Details

The list of all emails tagged as type = personal.

Example

  "personal_emails": [
    "[email protected]"
  ]

`phone_numbers`

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

Field Details

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

Example

  "phone_numbers": [
    "+15558675309"
  ]

`phones`

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

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.

Example

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

`recommended_personal_email`

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

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

  "recommended_personal_email": "[email protected]"

`work_email`

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

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

  "work_email": "[email protected]"

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`

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`.
Data Type	`Float`

Example

  "job_company_12mo_employee_growth_rate": -0.1379

`job_company_facebook_url`

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

Example

  "job_company_facebook_url": "facebook.com/peopledatalabs"

`job_company_founded`

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

Example

  "job_company_founded": 2015

`job_company_employee_count`

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

Example

  "job_company_employee_count": 125

`job_company_id`

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

Example

  "job_company_id": "tnHcNHbCv8MKeLh92946LAkX6PKg"

`job_company_industry`

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

Example

  "job_company_industry": "computer software"

`job_company_inferred_revenue`

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

Example

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

`job_company_linkedin_id`

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

Example

  "job_company_linkedin_id": "18170482"

`job_company_linkedin_url`

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

Example

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

`job_company_location_address_line_2`

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

Example

  "job_company_location_address_line_2": "suite 1670"

`job_company_location_continent`

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

Example

  "job_company_location_continent": "north america"

`job_company_location_country`

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

Example

  "job_company_location_country": "united states"

`job_company_location_geo`

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

Example

  "job_company_location_geo": "37.77,-122.41"

`job_company_location_locality`

Description	The person's current company's headquarters' locality.
Data Type	`String`

Example

  "job_company_location_locality": "san francisco"

`job_company_location_metro`

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

Example

  "job_company_location_metro": "san francisco, california"

`job_company_location_name`

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

Example

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

`job_company_location_postal_code`

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

Example

  "job_company_location_postal_code": "94105"

`job_company_location_region`

Description	The person's current company's headquarters' region.
Data Type	`String`

Example

  "job_company_location_region": "california"

`job_company_location_street_address`

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

Example

  "job_company_location_street_address": "455 market st"

`job_company_name`

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

Example

  "job_company_name": "people data labs"

`job_company_size`

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

Example

  "job_company_size": "51-200"

`job_company_ticker`

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

Example

  "job_company_ticker": "goog"

`job_company_total_funding_raised`

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

Example

  "job_company_total_funding_raised": 55250000.0

`job_company_twitter_url`

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

Example

  "job_company_twitter_url": "twitter.com/peopledatalabs"

`job_company_type`

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

Example

  "job_company_type": "public"

`job_company_website`

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

Example

  "job_company_website": "peopledatalabs.com"

Current Job

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

`inferred_salary`

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

Field Details

Must be one of our Canonical Inferred Salary Ranges.

Example

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

`job_last_changed`

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

Field Details

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

🚧
Limitations of Observed Data
This 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

  "job_last_changed": "2023-12-01"

`job_last_verified`

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

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

  "job_last_verified": "2024-01-05"

`job_onet_broad_occupation`

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

Example

  "job_onet_broad_occupation": "Chief Executives"

`job_onet_code`

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

Field Details

The 8-digit O*NET code for the person’s current job title, following the 2018 SOC guidelines.

Example

  "job_onet_code": "11-1011.00"

`job_onet_major_group`

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

Example

  "job_onet_major_group": "Management Occupations"

`job_onet_minor_group`

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

Example

  "job_onet_minor_group": "Top Executives"

`job_onet_specific_occupation`

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

Example

  "job_onet_specific_occupation": "Chief Executives"

`job_onet_specific_occupation_detail`

Description	A more detailed job title classification than O*NET Specific Occupation.
Data Type	`String`

Field Details

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.

Example

  "job_onet_specific_occupation_detail": "Chief Technology Officer"

`job_start_date`

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

Example

  "job_start_date": "2015-03"

`job_summary`

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

Field Details

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

Example

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

`job_title`

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

Field Details

The person's current job title.

Example

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

`job_title_class`

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

Field Details

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

Example

  "job_title_class": "research_and_development"

`job_title_levels`

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

Field Details

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

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

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

`job_title_role`

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

Field Details

The value will be one of our Canonical Job Roles.

🚧
Major Improvements Scheduled for v28.0 (October 2024)
In v28.0 (October 2024) we will be making significant improvements to our role and sub_role categorizations and changing the canonical values associated with these fields.
Please see our April 2024 Release Announcement (v26.0) for further information.

Example

  "job_title_role": "operations"

`job_title_sub_role`

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

Field Details

The value will be one of our Canonical Job Sub Roles. Each subrole maps to a role. See https://docs.peopledatalabs.com/docs/title-subroles-to-roles for a complete mapping list.

🚧
Major Improvements Scheduled for v28.0 (October 2024)
In v28.0 (October 2024) we will be making significant improvements to our role and sub_role categorizations and changing the canonical values associated with these fields.
Please see our April 2024 Release Announcement (v26.0) for further information.

Example

  "job_title_sub_role": "logistics"

Demographics

`birth_date`

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

Field Details

If this field exists, birth_year will agree with it.

Example

  "birth_date": "1990-12-02"

`birth_year`

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

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

  "birth_year": 1990

`sex`

🚧
gender was renamed to sex in v26.0
In 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.

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

Field Details

The value will always be one of our Canonical Sex.

Example

  "sex": "male"

`languages`

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

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).

Example

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

Education

`education`

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

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.

`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

  "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 https://docs.peopledatalabs.com/docs/data-types#locations.

`countries`

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

Example

  "countries": [
    "united states"
  ]

`location_address_line_2`

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

Example

  "location_address_line_2": "apartment 12"

`location_continent`

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

Example

  "location_continent": "north america"

`location_country`

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

Example

  "location_country": "united states"

`location_geo`

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

Example

  "location_geo": "37.87,-122.27"

`location_last_updated`

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

Field Details

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

Example

  "location_last_updated": "2018-11-05"

`location_locality`

Description	The locality of the person's current address.
Data Type	`String`

Example

  "location_locality": "berkeley"

`location_metro`

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

Example

  "location_metro": "san francisco, california"

`location_name`

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

Example

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

`location_names`

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

Example

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

`location_postal_code`

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

Example

  "location_postal_code": "94704"

`location_region`

Description	The region of the person's current address.
Data Type	`String`

Example

  "location_region": "california"

`location_street_address`

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

Example

  "location_street_address": "455 fake st"

`regions`

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

Example

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

`street_addresses`

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

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.

Example

  "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`

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

Field Details

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

Example

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

`possible_emails`

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

Field Details

This field uses the same format as the emails field.

Example

  "possible_emails": [
    {
      "address": "[email protected]",
      "type": null,
      "first_seen": "2021-06-13",
      "last_seen": "2021-06-13",
      "num_sources": 2
    }
  ]

`possible_location_names`

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

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.

Example

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

`possible_phones`

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

Field Details

This field uses the same format as the phones field.

Example

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

`possible_profiles`

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

Field Details

This field uses the same format as the profiles field.

Example

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

`possible_street_addresses`

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

Field Details

This field uses the same format as the street_addresses field.

Example

  "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": "2021-06-13",
      "num_sources": 2
    }
  ]

Social Presence

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`

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

Example

  "facebook_friends": 3912

`facebook_id`

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

Example

  "facebook_id": "1089351304"

`facebook_url`

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

Example

  "facebook_url": "facebook.com/deseanthorne"

`facebook_username`

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

Example

  "facebook_username": "deseanthorne"

`github_url`

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

Example

  "github_url": "github.com/deseanathan_thornolotheu"

`github_username`

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

Example

  "github_username": "deseanathan_thornolotheu"

`linkedin_connections`

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

Field Details

Typically between 0-500.

Example

  "linkedin_connections": 432

`linkedin_id`

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

Example

  "linkedin_id": "145991517"

`linkedin_url`

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

Example

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

`linkedin_username`

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

Example

  "linkedin_username": "seanthorne"

`profiles`

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

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.

Example

  "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`

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

Example

  "twitter_url": "twitter.com/seanthorne5"

`twitter_username`

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

Example

  "twitter_username": "seanthorne5"

Work History

`certifications`

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

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.

Example

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

`experience`

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

Field Details

The order of work experience in the list is determined by recency and associativity. 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 (if different from the company HQ).
`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.

`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.
`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 .

Example

  "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`

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

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

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

`industry`

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

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

  "industry": "computer software"

`inferred_years_experience`

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

Field Details

The value will be between 0 and 100.

Example

  "inferred_years_experience": 7

`interests`

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

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.

Example

  "interests": [
    "data"
  ]

`job_history`

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

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.

Example

  "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`

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

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.

The Skill Enrichment API can help find similar skills.

Example

  "skills": [
    "entrepreneurship"
  ]

`summary`

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

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

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

PDL Record Information & Metadata

`dataset_version`

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

Field Details

This field currently exists in Person Enrichment API responses. (Person Search and Person Identify APIs use version_status).

Note: This number corresponds to the data release number, not the API release number.

Example

  "dataset_version": "19.2"

`first_seen`

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

Example

  "first_seen": "2017-06-02"

`num_records`

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

Example

  "num_records": 420

`num_sources`

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

Example

  "num_sources": 72

`operation_id`

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

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

  "operation_id": "acee3bde2e1a2cb7e75c57b80d5b7bc2d5de5b02e7ea51f91304c28df77251dc"

`version_status`

❗️
DEPRECATION NOTICE
This object has been deprecated in API responses.

Description	Metadata about the version of the dataset used to retrieve this record.
Data Type	`Object`

Field Details

This object tracks the pervious and current dataset version, and any other persistent IDs that were merged into this record using improved entity resolution and the status of the record.

Field	Data Type	Description
`contains`	`Array [String]`	The list of IDs that have been merged into this record since the last release.
`current_version`	`String`	The current data version.
`previous_version`	`String`	The previous data version.
`status`	`Enum (String)`	The changes made to this record between the previous release and the current one. One of our Canonical Version Statuses.

Example

  "version_status": {
    "contains": [
      "qEnOZ5Oh0poWnQ1luFBfVw_0000"
    ],
    "current_version": "15",
    "previous_version": "14",
    "status": "updated"
  }

Overview

📘Field Availability

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_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

Example

job_company_location_metro

Example

📘
Field Availability

`first_name`

`full_name`

`id`

`last_initial`

`last_name`

`middle_initial`

`middle_name`

`name_aliases`

`emails`

`mobile_phone`

`personal_emails`

`phone_numbers`

`phones`

`recommended_personal_email`

`work_email`

`job_company_12mo_employee_growth_rate`

`job_company_facebook_url`

`job_company_founded`

`job_company_employee_count`

`job_company_id`

`job_company_industry`

`job_company_inferred_revenue`

`job_company_linkedin_id`

`job_company_linkedin_url`

`job_company_location_address_line_2`

`job_company_location_continent`

`job_company_location_country`

`job_company_location_geo`

`job_company_location_locality`

`job_company_location_metro`

`job_company_location_name`

`job_company_location_postal_code`

`job_company_location_region`

`job_company_location_street_address`

`job_company_name`

`job_company_size`

`job_company_ticker`

`job_company_total_funding_raised`

`job_company_twitter_url`

`job_company_type`

`job_company_website`

`inferred_salary`

`job_last_changed`

🚧
Limitations of Observed Data

`job_last_verified`

`job_onet_broad_occupation`

`job_onet_code`

`job_onet_major_group`

`job_onet_minor_group`

`job_onet_specific_occupation`

`job_onet_specific_occupation_detail`

`job_start_date`

`job_summary`

`job_title`