Profile

Overview

The Profile dataset represents labor supply signals derived from professional profile records. In Lightcast MCP, the dataset identifier is profiles.

This dataset provides insight into workforce composition through self-reported professional backgrounds, anonymized work history, skills, education, career progression, employer affiliations, geographic distribution, and estimated wage signals.

Purpose

Use the Profile dataset to understand talent supply and workforce composition.

It is designed to answer questions about the skills people have, where talent is located, which employers or schools are represented within a workforce segment, and how professional backgrounds relate to occupations, industries, and career pathways.

Use this dataset when the question is about people, qualifications, or workforce supply.

What the Dataset Measures

Business Meaning	Description
Talent Supply	Professional profile records that represent available workforce signals.
Workforce Composition	Skills, education, employer affiliation, occupation, industry, and geography.
Skills and Experience	Skills listed by professionals and years of occupational experience.
Education Background	Schools, education levels, programs, and graduation years.
Career Progression	Work history and employer affiliation signals.
Estimated Wages	Median estimated wage and estimated wage distributions where supported.

Common Use Cases

Workforce Skills Analysis

Identify skills associated with an occupation, employer, or region.

Talent Market Sizing

Count profiles matching a skill, occupation, industry, company, or geography.

Employer Affiliation Analysis

Understand companies represented in a workforce segment.

Education Pipeline Analysis

Rank schools or education programs associated with a workforce segment.

Wage Signal Analysis

Estimate or distribute wage signals for filtered profile populations.

Example Questions

01/Which skills do computer programmers have at a given employer?

Analyze the skills associated with computer programmers at a selected employer. Compare the workforce's skill composition within the organization. Related metrics: profiles.unique_profiles, profiles.unique_companies.

02/Which companies in Seattle have the most profiles for nurses?

Rank companies in Seattle by the number of nursing profiles. Identify employers with the largest representation of nursing professionals. Related metrics: profiles.unique_companies, profiles.unique_profiles.

03/What skills do data scientists typically claim to have?

Analyze the skills listed by professionals working as data scientists. Identify the skills most commonly represented in the workforce. Related metrics: profiles.unique_profiles.

04/What are the top schools attended by mechanical engineers in California?

Rank the educational institutions attended by mechanical engineers in California. Identify the schools most commonly represented in the workforce. Related metrics: profiles.unique_schools, profiles.unique_profiles.

Available Dimensions

Dimension	Description
`company`	Company name normalized through a company taxonomy.
`company_employee_count_band`	Company employee count band.
`company_flags`	Company flags such as Fortune-list indicators.
`company_revenue_band`	Company revenue band.
`education_grad_year`	Education graduation year range.
`education_program_cip`	Classification of Instructional Programs.
`education_school`	Education school taxonomy.
`experience`	Years of experience in occupation.
`industry_naics`	North American Industry Classification System.
`location_us_area`	US area hierarchy, including state, MSA, and county.
`location_us_city`	US city hierarchy.
`occupation_lot`	Lightcast Occupation Taxonomy.
`occupation_onet`	O*NET occupation classification.
`occupation_soc`	Standard Occupational Classification.
`profiles_education_level`	Education level facet for profiles.
`skill`	Lightcast skill taxonomy.
`time`	Profile last-updated time filter.

For dimension definitions and filtering guidance, see Dimensions.

Available Metrics

Metric	Description
`profiles.unique_profiles`	Count of distinct profiles.
`profiles.unique_schools`	Count of distinct schools represented in matching profiles.
`profiles.unique_companies`	Count of distinct companies represented in matching profiles.
`profiles.median_estimated_wage`	Median estimated wage for matching profiles.
`profiles.estimated_wage`	Estimated wage domain metric for distributions.

For metric definitions and usage guidance, see Metrics.

Supported MCP Tools

Tool	Supported
`count`	Yes
`distribution`	Yes
`estimate`	Yes
`list`	No
`timeseries`	No
`top`	Yes