Lightcast US Profiles APIs help you access, analyze, and distribute profile data across a range of workforce use cases. They allow you to retrieve individual professional profiles, rank profiles by location, evaluate data recency, and understand how profiles are distributed across populations, all grounded in U.S. labor market data for consistent, reliable insights.
Explore the use cases below to understand how the US Profiles API can be applied in real-world scenarios.
Rank Profile by Dimensions
Profile data can provide valuable insight into where specialized talent is concentrated and how training programs are distributed across the US market. The US Profiles Rankings API allows you to analyze profile data by geographic dimensions such as cities, helping you identify regions with high certification activity and strong educational representation.
This API lets you group and rank US profiles based on how frequently each profile appears across US talent profiles. It provides a ranked list along with profile counts and the number of unique companies where each certification appears, giving you a clear picture of real-world certification demand in the US.
See the Profiles Rankings API reference for full request parameters and response details.
When to Use
-
You want to identify the most common profiles in the US workforce
-
You need to decide which profiles to require or recommend for a role in the US
-
You are aligning learning or hiring programs with the US labor market data
Endpoint
https://api.lightcast.io/profiles/rankings/{rankingFacet}Sample Request
curl --request POST \
--url https://api.lightcast.io/profiles/rankings/certifications \
--header 'accept: application/json' \
--header 'authorization: Bearer XXXXX ' \
--header 'content-type: application/json'Sample Response
{
"data": {
"ranking": {
"buckets": [
{
"name": "San Francisco, CA",
"profiles": 95,
"unique_schools": 57
},
{
"name": "Davis, CA",
"profiles": 73,
"unique_schools": 44
},
{
"name": "Sacramento, CA",
"profiles": 35,
"unique_schools": 20
},
{
"name": "San Jose, CA",
"profiles": 13,
"unique_schools": 10
},
{
"name": "Los Angeles, CA",
"profiles": 11,
"unique_schools": 11
},
{
"name": "San Diego, CA",
"profiles": 7,
"unique_schools": 4
},
{
"name": "New York, NY",
"profiles": 6,
"unique_schools": 12
},
{
"name": "Fremont, CA",
"profiles": 4,
"unique_schools": 4
},
{
"name": "Campbell, CA",
"profiles": 3,
"unique_schools": 3
},
{
"name": "Castro Valley, CA",
"profiles": 3,
"unique_schools": 2
},
{
"name": "Milpitas, CA",
"profiles": 3,
"unique_schools": 1
},
{
"name": "Oakland, CA",
"profiles": 3,
"unique_schools": 4
},
{
"name": "Orange, CA",
"profiles": 3,
"unique_schools": 9
},
{
"name": "Palo Alto, CA",
"profiles": 3,
"unique_schools": 5
},
{
"name": "Santa Clara, CA",
"profiles": 3,
"unique_schools": 5
},
{
"name": "Washington, DC",
"profiles": 3,
"unique_schools": 7
},
{
"name": "Berkeley, CA",
"profiles": 2,
"unique_schools": 4
},
{
"name": "Cupertino, CA",
"profiles": 2,
"unique_schools": 2
},
{
"name": "Dublin, CA",
"profiles": 2,
"unique_schools": 3
},
{
"name": "Elk Grove, CA",
"profiles": 2,
"unique_schools": 2
}
],
"facet": "city_name",
"limit": 20,
"rank_by": "profiles"
},
"totals": {
"profiles": 344,
"unique_schools": 164
}
}
}Profile Activity Over Time
Understanding the current talent pool requires more than a snapshot of total profile counts. Without visibility into when profiles were last active or created, it is difficult to assess whether workforce data reflects today's labor market or is skewed by older, potentially outdated records across the United States.
The Profiles Recency API enables you to evaluate the freshness of profile data by breaking down profile volume and unique school representation year over year. It provides annual counts of profiles and associated schools, giving you a clear view of how data coverage has grown or shifted over time across U.S. talent pools.
See the Profiles Recency API reference for full request parameters and response details.
When to Use
- You want to assess how current your profile data is across U.S. labor markets
- You need to identify whether workforce insights are driven by recent or historical profiles
- You are tracking year-over-year growth in credentialed talent across domestic institutions
- You want to validate data freshness before using it for workforce planning or recruitment decisions
Endpoint
https://api.lightcast.io/profiles/recencySample Request
curl --request POST \
--url https://api.lightcast.io/profiles/recency \
--header 'accept: application/json' \
--header 'authorization: Bearer XXXX' \
--header 'content-type: application/json'Sample Response
{
"data": {
"recency": {
"profiles": [
0,
8,
144
],
"unique_schools": [
0,
12,
126
],
"year": [
"2019",
"2020",
"2021"
]
},
"totals": {
"profiles": 152,
"unique_schools": 126
}
}
}Match Profile by ID
Analyzing talent pools often requires access to a complete, structured view of an individual professional's background. Without a reliable way to retrieve this data by identifier, it is difficult to surface the full context behind a profile, including education history, employment, skills, location, and occupation classification, across U.S. talent pools.
The US Profiles API enables you to retrieve a single professional profile by ID, returning a comprehensive record that includes standardized location data, education history, employment history, skills mapped to the Lightcast taxonomy, occupation classifications, estimated compensation, and personal contact information. This gives you a detailed, structured view of an individual professional anywhere across the United States.
See the US Profiles API reference for full request parameters and response details.
When to Use
- You want to retrieve a complete profile record for a specific individual in your dataset
- You need to surface standardized occupation, skill, and location data for a known professional
- You are building a talent profile view within a workforce or recruitment platform
- You want to cross-reference individual profile data against labor market benchmarks across the US
Endpoint
https://api.lightcast.io/profiles/profiles/{profile_id}Sample Request
curl --request GET \
--url https://api.lightcast.io/profiles/profiles/profile_id \
--header 'accept: application/json' \
--header 'authorization: Bearer XXXX'Sample Response
{
"data": {
"acquired_timestamp": "2020-01-01",
"city": "U2FuIEZyYW5jaXNjbw==",
"city_name": "San Francisco, CA",
"company": "123456789",
"company_name": "Acme Corporation",
"country": "US",
"county": 6075,
"county_name": "San Francisco",
"laa_country": "US",
"laa_country_name": "United States",
"laa_admin_area_1": "US_WEST",
"laa_admin_area_1_name": "US West",
"laa_admin_area_2": "US_CA_SF",
"laa_admin_area_2_name": "San Francisco County",
"laa_metro": "US_SF",
"laa_metro_name": "San Francisco Metro",
"educations": [
{
"cip6_name": null,
"level_name": null,
"field_raw": null,
"grad_year": null,
"level_raw": null,
"school_id": "53bff579e4b04710d09fbd9c",
"school_name": "Stanford University",
"school_raw": "stanford university"
},
{
"cip6_name": null,
"level_name": null,
"field_raw": "computer science",
"grad_year": null,
"level_raw": "bachelor",
"school_id": "53bff579e4b04710d09fdac8",
"school_name": "University of California, Berkeley",
"school_raw": "university of california berkeley"
}
],
"estimated_wage_p50": 125000,
"id": "1111111111111",
"last_updated": "2024-01-01",
"lot_career_area": "33",
"lot_career_area_name": "Science and Research",
"lot_occupation": "331312",
"lot_occupation_group": "3313",
"lot_occupation_group_name": "General Research",
"lot_occupation_name": "Researcher / Research Associate",
"lot_specialized_occupation": "33131215",
"lot_specialized_occupation_name": "Research Associate (General)",
"msa": 41860,
"msa_name": "San Francisco-Oakland-Hayward, CA",
"naics6": "541511",
"naics6_name": "Custom Computer Programming Services",
"onet": "15-1132.00",
"onet_name": "Software Developers, Applications",
"personal_info": {
"emails": [
"[email protected]"
],
"employment_history": [
{
"company_name_raw": "acme corporation",
"description": null,
"job_end_ym": null,
"job_start_ym": "2020-01",
"title_raw": "Senior Software Developer"
},
{
"company_name_raw": "tech solutions inc",
"description": "Developed and maintained web applications using modern frameworks.",
"job_end_ym": "2020-01",
"job_start_ym": "2018-01",
"title_raw": "Software Developer"
}
],
"job_description": "",
"name": "John Doe",
"first_name": "John",
"last_name": "Doe",
"names": [
"John Doe"
],
"phones": [
"+1 415-555-1234"
],
"profile_urls": [],
"title_raw": "Senior Software Developer"
},
"skills": [
"KS440MC61Q7HPDQR5YHT",
"KS127Z65Y2BBKJZ3W2HL",
"KS1200365FTR9X0M96T9",
"KS127ZG6DBSF76WF7VYX",
"KS121YX5XHV5QPYV2TXL",
"KS1200H6XYN1CR0G5NZ0",
"KS1261G6FLXL70JW7G42",
"KS441626TDSV2J6GBB86",
"KS1254Q675CFWDY8PR54",
"KS120BV6SR75RBKQH0G3",
"KS126HY6YLTB9R7XJC4Z",
"KS125ZB6BWF5RY40BH1B",
"KS122556LMQ829GZCCRV",
"KS1282N6NQMZ95M1HJ7L",
"KS123NJ76GJLK91JS75M",
"KS1267F6MSPN366LX7ST"
],
"skills_name": [
"JavaScript",
"Python",
"React",
"Node.js",
"SQL",
"Git",
"Docker",
"AWS",
"RESTful APIs",
"Agile Methodology",
"Test-Driven Development",
"Code Review",
"Microservices",
"CI/CD",
"MongoDB",
"TypeScript"
],
"state": 6,
"state_name": "California",
"title": "ETCCFF59C12964DA3C",
"title_name": "Software Developers",
"title_raw": "Senior Software Developer"
}
}Analyze Profile Distributions
Understanding how profiles are spread across a talent population requires more than total counts. Without a structured view of how professionals are distributed across defined segments, it is difficult to assess talent concentration, identify gaps, or compare profile density.
The Profiles Distributions API enables you to analyze how U.S. professional profiles are distributed across defined ranges by returning data broken down into histogram buckets. Each bucket includes a profile count and the number of unique companies represented, giving you a clear picture of where professionals are concentrated across a talent population.
See the Profiles Distributions API reference for full request parameters and response details.
When to Use
- You want to understand how profiles are concentrated across defined segments within a U.S. talent population
- You need to identify which ranges have the highest or lowest profile density
- You are comparing profile distribution across segments for workforce planning or recruitment
Endpoint
https://api.lightcast.io/profiles/distributions/{distributionFacet}Sample Request
curl --request POST \
--url https://api.lightcast.io/profiles/distributions/estimated_wage \
--header 'accept: application/json' \
--header 'authorization: Bearer XXXXXX' \
--header 'content-type: application/json' \
--data '
{
"distribution": {
"type": "histogram"
}
}
'Sample Response
{
"data": {
"distribution": {
"buckets": [
{
"key": 25,
"profiles": 2,
"unique_companies": 3,
"value": 56064
},
{
"key": 50,
"profiles": 6,
"unique_companies": 5,
"value": 82688
},
{
"key": 75,
"profiles": 9,
"unique_companies": 6,
"value": 94976
}
],
"domain": {
"max": 174054,
"min": 47236
},
"facet": "estimated_wage",
"interval": null,
"type": "percentile"
},
"totals": {
"profiles": 18,
"unique_companies": 16
}
}
}