PDL Scores for Person Data (Early Access)

📘
Early Access
PDL Scores for Person Data are launching in Early Access with the June 2026 Release (v34.2). They will be available on records in PDL's Resume dataset.

Overview

PDL Scores are generated scoring attributes available across PDL datasets. This Early Access release includes PDL Scores for Person Data.

Early Access includes two scores and two supporting Score Factor objects:

Attribute	What it is
`profile_score`	A bucketed signal indicating whether a profile appears to represent a real person
`profile_score_factors`	Supporting signals used to generate `profile_score`
`activity_score`	A bucketed signal indicating whether a profile appears to be actively maintained
`activity_score_factors`	Supporting signals used to generate `activity_score`

Use scores when you want ready-to-use profile quality or activity signals. Use score factors when you want more transparency into the signals behind a score or want to build custom logic.

For the complete list of new attributes, see Field Overview. For descriptions and examples, see Field Details.

Example Response

{
  "profile_score": "positive signals",
  "profile_score_factors": {
    "attribute_fill_rate": 0.625,
    "profile_age_months": 110,
    "has_valid_url": 1,
    "meets_connection_threshold": 1
  },
  "activity_score": "neutral signals",
  "activity_score_factors": {
    "connection_change": 0.5,
    "profile_change": 1,
    "months_since_last_end_resume": 0
  }
}

Field Overview

Field	Type	Short Description
`profile_score`	`String`	A PDL-generated signal indicating how likely a profile is to represent a real person
`profile_score_factors`	`Object`	Supporting factors used to generate `profile_score`
`profile_score_factors.attribute_fill_rate`	`Float`	The share of selected resume attributes present on the profile
`profile_score_factors.profile_age_months`	`Integer`	The age of the profile in months, based on PDL's first observation of the trusted LinkedIn profile
`profile_score_factors.has_valid_url`	`Float`	A signal indicating whether the profile has a valid LinkedIn URL
`profile_score_factors.meets_connection_threshold`	`Integer`	A signal indicating whether the profile meets PDL's LinkedIn connection threshold
`activity_score`	`String`	A PDL-generated signal indicating how likely a profile is to be actively maintained
`activity_score_factors`	`Object`	Supporting factors used to generate `activity_score`
`activity_score_factors.connection_change`	`Float`	A signal indicating whether PDL has observed changes in the profile's LinkedIn connection count
`activity_score_factors.profile_change`	`Integer`	A signal indicating whether PDL has observed user-edited profile changes recently
`activity_score_factors.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

Availability

⚠️
Resume Slice Only
The profile_score and activity_score are only populated for records in the Resume Slice of the Person dataset, i.e. records with a linkedin_url. The scores rely on resume and LinkedIn-related signals, such as LinkedIn URL validity and connection activity.

Profiles outside the Resume Slice will return null for the scores and factor objects.

The following scores are included with existing Person Data bundles during Early Access:

profile_score
activity_score

The following Score Factor objects are available as an add-on:

profile_score_factors
activity_score_factors

Common Workflows

Customers can use PDL Person Scores to:

Filter or suppress low-signal profiles before ingestion
Rank higher-confidence profiles first in search or matching workflows
Route negative-signal profiles to manual review
Improve entity resolution by using profile_score and activity_score as tiebreakers
Prioritize outreach toward profiles that appear more active
Reduce storage, compute, and maintenance costs associated with low-quality records

Field Details

`profile_score`

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

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`	The profile is outside the Resume dataset

Example

"profile_score": "positive signals"

`profile_score_factors`

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

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 as an add-on. PDL does not expose the raw score used to assign the final profile_score bucket.

Example

"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`

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

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

"attribute_fill_rate": 0.625

`profile_score_factors.profile_age_months`

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

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

"profile_age_months": 110

`profile_score_factors.has_valid_url`

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

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

"has_valid_url": 1

`profile_score_factors.meets_connection_threshold`

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

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 connection threshold

Example

"meets_connection_threshold": 1

`activity_score`

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

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, or the profile is outside the Resume dataset

Example

"activity_score": "neutral signals"

`activity_score_factors`

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

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 as an add-on. PDL does not expose the raw score used to assign the final activity_score bucket.

Example

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

`activity_score_factors.connection_change`

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

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

"connection_change": 0.5

`activity_score_factors.profile_change`

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

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 profile change

Example

"profile_change": 1

`activity_score_factors.months_since_last_end_resume`

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

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 value may be 0. 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

"months_since_last_end_resume": 0