Use Cases

Lightcast Global Profiles APIs help you retrieve, analyze, and rank workforce profile data across a range of dimensions, including job titles, skills, and career history. They allow you to access both individual profile details and aggregated workforce insights, enabling consistent data, better talent decisions, and more reliable labor market analytics.

Explore the use cases below to understand how the Global Profiles API can be applied in real-world scenarios.

Profile Rank

Knowing which profiles are most common in the workforce helps organizations make better decisions about hiring, learning programs, and role requirements. Without reliable data, these decisions are often based on assumptions rather than actual market trends.

The Profile Ranking API lets you rank profiles based on how frequently they appear across global talent profiles. It returns a ranked list along with profile counts and the number of unique companies where each profile appears, giving you a clear picture of real-world demand.

See the Profile Ranking API reference for full request parameters and response details.

When to Use

You want to identify the most common profiles in the workforce
You need to decide which profile to require or recommend for a role
You are aligning learning or hiring programs with real labor market data

Endpoint

https://api.lightcast.io/global-profiles/rankings/{rankingFacet}

Sample Request

curl --request POST \
     --url https://api.lightcast.io/global-profiles/rankings/certifications \
     --header 'accept: application/json' \
     --header 'authorization: Bearer XXXXXX' \
     --header 'content-type: application/json' \
     --data '
{
  "rank": {
    "by": "profiles",
    "limit": 10,
    "extra_metrics": [
      "profiles"
    ],
    "min_profiles": 1
  }
}
'

Sample Response

{
  "data": {
    "ranking": {
      "buckets": [
        {
          "name": "Software Engineer",
          "profiles": 3384,
          "unique_companies": 1364
        },
        {
          "name": "Software Developer",
          "profiles": 3308,
          "unique_companies": 1817
        },
        {
          "name": "Analyst/Manager",
          "profiles": 3004,
          "unique_companies": 821
        },
        {
          "name": ".NET Software Engineer",
          "profiles": 2650,
          "unique_companies": 881
        },
        {
          "name": "Business Analyst",
          "profiles": 1958,
          "unique_companies": 1168
        }
      ],
      "facet": "title_name",
      "limit": 5,
      "rank_by": "profiles"
    },
    "totals": {
      "profiles": 125286,
      "unique_companies": 20934
    }
  }
}

Profile Recency

Understanding how profiles have been updated over time helps organizations assess the freshness and relevance of talent profiles in their analysis. Without this visibility, it is difficult to know whether insights are being driven by current trends or outdated data.

The Profile Recency API lets you group and rank profiles by the year they were last active, based on how frequently profiles appear across each time period. It returns a year-by-year breakdown of profile counts, giving you a clear picture of how current and representative your talent data is.

See the Profile Recency API reference for full request parameters and response details.

When to Use

You need to assess the freshness of profiles before running the analysis
You want to identify whether your insights are driven by recent or older data
You are filtering or weighting profiles based on recency for more accurate results

Endpoint

https://api.lightcast.io/global-profiles/recency

Sample Request

curl --request POST \
     --url https://api.lightcast.io/global-profiles/recency \
     --header 'accept: application/json' \
     --header 'authorization: Bearer XXXXXXXX' \
     --header 'content-type: application/json'

Sample Response

{
  "data": {
    "recency": {
      "profiles": [
        2855,
        3390,
        2937,
        20650,
        21280,
        243016
      ],
      "year": [
        "2020",
        "2021",
        "2022",
        "2023",
        "2024",
        "2025"
      ]
    },
    "totals": {
      "profiles": 294128
    }
  }
}

Match Profile by ID

Workforce decisions are most effective when they are grounded in detailed, structured data about individual professionals. Without access to comprehensive profile information, it is difficult to understand a person's skills, experience, education, and career trajectory in a consistent and analyzable format.

This API lets you retrieve a complete talent profile by profile ID, returning structured data across all key dimensions of a professional's background. It provides standardized fields including skills, employment history, education, job titles, location, industry classifications, and more, giving you a full picture of an individual's workforce presence.

See the Global Profiles API reference for full request parameters and response details.

When to Use

You want to retrieve detailed profile data for a specific individual
You need to analyze a professional's skills, titles, and career history in a structured format
You want to enrich your internal records with standardized workforce data

Endpoint

https://api.lightcast.io/global-profiles/profiles/{profileId}

Sample Request

curl --request GET \
     --url https://api.lightcast.io/global-profiles/profiles/profileId \
     --header 'accept: application/json' \
     --header 'authorization: Bearer XXXXXX'

Sample Response


  "data": {
    "acquired_timestamp": "1999-12-31",
    "city": "VG9yb250bywgVG9yb250byBEaXZpc2lvbg==",
    "city_name": "Toronto, Toronto Division",
    "company": "37493873",
    "company_name": "General Motors",
    "educations": [
      {
        "field_raw": null,
        "grad_year": "1980-01",
        "level_name": "Bachelor's Degree",
        "level_raw": "bachelor of science|bachelors",
        "school_id": "53bff579e4b04710d09fdcc7",
        "school_name": "University of Belgrade",
        "school_raw": "university of belgrade"
      }
    ],
    "id": "3f953ad37c3c87e90481ee08ed0352a8",
    "laa_admin_area_1": "CA35",
    "laa_admin_area_1_name": "Ontario",
    "laa_admin_area_2": "CA3521024",
    "laa_admin_area_2_name": "Caledon",
    "laa_country": "CA",
    "laa_country_name": "Canada",
    "laa_metro": "CA35535",
    "laa_metro_name": "Toronto",
    "language": "en",
    "last_updated": "2025-10-08",
    "lot_career_area": "18",
    "lot_career_area_name": "Engineering",
    "lot_occupation": "181111",
    "lot_occupation_group": "1811",
    "lot_occupation_group_name": "Chemical, Biomedical, and Related Engineering",
    "lot_occupation_name": "Chemical / Process Engineer",
    "lot_specialized_occupation": "18111114",
    "lot_specialized_occupation_name": "Process Control Engineer",
    "naics6": "336390",
    "naics6_name": "Other Motor Vehicle Parts Manufacturing",
    "onet": "99-9999.00",
    "onet_name": "Unclassified",
    "personal_info": {
      "emails": [],
      "employment_history": [
        {
          "company_name_raw": "general motors",
          "description": "GA Engineering - Chassis Controls Engineer",
          "job_end_ym": null,
          "job_start_ym": "2010-03",
          "title_raw": "ga engineering - chassis controls engineer"
        },
        {
          "company_name_raw": "gm oshawa truck plant",
          "description": null,
          "job_end_ym": "2009-05",
          "job_start_ym": "1996-03",
          "title_raw": "manufacturing engineer - controls"
        }
      ],
      "first_name": "Stevan",
      "job_description": "GA Engineering - Chassis Controls Engineer",
      "last_name": "Hinic",
      "name": "Stevan Hinic",
      "names": [
        "Stevan Hinic"
      ],
      "phones": [],
      "profile_urls": [
        "linkedin.com/in/stevanhinic"
      ],
      "title_raw": "ga engineering - chassis controls engineer"
    },
    "skills": [
      "KS1201W79NJW2J5ZQDFV",
      "KS12027766237GNG6G37",
      "KS123K35XW0ZY6YB2QTW",
      "KS122C06Z8NDM5G6NYNT",
      "KS120LH6DHTBJYVLFKB3",
      "KS123F95Y4BFF41P3Z6C",
      "KS1281Y6QHJ8PQLHTZ6R",
      "KS120SQ5W4Q57JMD2Y81",
      "KS4404475JBQ0GSP7CX1",
      "KS7G2RS68WTQ4B3H2KMQ",
      "KS1267F6MSPN366LX7ST",
      "KS127WQ632MH4X9JDYN9",
      "KS441K2756CXYXBG990G",
      "KS122W26SKGLZJ2KLF5M",
      "KS1264H778JLQY5FK48G",
      "KS681KH79MJLQDPB9T88",
      "KS440TB6786R7HHQ2Z7D",
      "KS7G3VS6MGJLL0NX5BY6",
      "KS1241478VQ66G2NPCG9",
      "KS125RZ5X2MBLVC6F634",
      "ESB6F489A434444F26E7",
      "KS124R469YBN5SX8XNMH"
    ],
    "skills_name": [
      "Six Sigma Methodology",
      "Quality Management Systems",
      "Process Engineering",
      "Continuous Improvement Process",
      "Electrical Engineering",
      "Electronic Engineering",
      "Process Control",
      "Automation",
      "Robotics",
      "Root Cause Analysis",
      "Project Management",
      "Programmable Logic Controllers",
      "Troubleshooting (Problem Solving)",
      "Project Planning",
      "Manufacturing Engineering",
      "Process Improvement",
      "Statistical Process Controls",
      "Commissioning",
      "Failure Mode And Effects Analysis",
      "Lean Manufacturing",
      "Kaizen Methodology",
      "Supervisory Control And Data Acquisition (SCADA)"
    ],
    "title": "ET221113498FB194EB",
    "title_name": "Controls Engineers",
    "title_raw": "ga engineering - chassis controls engineer"
  }
}

Additional Resources

View full API reference