Job Posting

Overview

The Job Posting dataset represents real-time labor demand from deduplicated online job advertisements. In Lightcast MCP, its dataset identifier is job_postings.

This dataset provides insight into employer hiring activity, required skills, qualifications, occupations, industry context, geographic demand, compensation signals, and labor market trends.

Job posting analysis tools require a time filter. For filter requirements and examples, see Dimensions.

Purpose

Use the Job Posting dataset to analyze employer demand and hiring behavior.

It is designed to answer questions about what employers are hiring for, which skills and qualifications they request, how demand changes over time, and how hiring activity varies across occupations, industries, companies, and locations.

What the Dataset Measures

Business Meaning	Description
Labor Demand	Demand represented by online job advertisements.
Hiring Activity	Posting counts, unique companies, duplicate postings, and posting intensity.
Employer Requirements	Skills, education, experience, occupation, employment type, seniority, and contract type.
Compensation Signals	Advertised salary metrics and salary distributions where available.
Market Trends	Posting volume and salary metrics over time.
Posting Duration	How long postings stay active, where supported by metrics.

Common Use Cases

Skills Demand Analysis

Identify skills requested in postings for an occupation, employer, or region.

Employer Activity Tracking

Rank companies by posting volume or unique companies in a market.

Compensation Benchmarking

Analyze advertised salary metrics for filtered posting markets.

Demand Trend Monitoring

Track posting volume or salary metrics over time.

Market Comparison

Compare demand across occupations, industries, locations, or skills.

Posting Sample Review

Inspect small samples of matching job postings when granular posting fields are needed.

Example Questions

01/Which skills are employers seeking for computer programmers?

Analyze the skills most frequently requested in job postings for computer programmers. Identify the skills employers request most often across the selected market. Related metrics: postings.unique_postings, postings.total_postings, postings.posting_intensity.

02/Which companies in Seattle are hiring the most nurses?

Rank companies in Seattle by hiring activity for nursing positions. Identify the employers with the highest number of job postings in the selected location. Related metrics: postings.unique_companies, postings.unique_postings, postings.total_postings.

03/What skills do employers most often associate with data scientist occupations?

Analyze the skills most commonly associated with data scientist job postings. Identify the skills employers expect for the occupation. Related metrics: postings.unique_postings, postings.total_postings, postings.posting_intensity.

04/Has demand increased or decreased for JavaScript in the last 6 months?

Track hiring demand for JavaScript over the selected time period. Identify increases, decreases, and seasonal hiring patterns. Related metrics: postings.unique_postings, postings.total_postings, postings.posting_intensity.

05/How long does the average web developer posting stay up in California?

Analyze how long web developer job postings remain active in California. Compare posting duration across occupations, locations, or time periods. Related metrics: postings.median_posting_duration, postings.posting_duration.

Available Dimensions

Dimension	Description
`benefit`	Job posting benefits taxonomy.
`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.
`compensation_salary`	Salary range filter using lower and/or upper bounds.
`employment_contract_type`	Employment contract type, such as temporary or permanent.
`education_program_cip`	Classification of Instructional Programs.
`employment_job_seniority`	Job seniority, such as junior, intermediate, or senior.
`employment_type`	Employment type, such as full-time or part-time.
`experience`	Minimum years of experience listed on postings.
`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.
`postings_education_level`	Education level facet for job postings.
`posting_flags`	Posting flags such as internship and supervisor indicators.
`skill`	Lightcast skill taxonomy.
`time`	Posting time filter. Required for analysis tools.

For dimension definitions and filtering guidance, see Dimensions.

Available Metrics

Metric	Description
`postings.unique_postings`	Count of distinct deduplicated job postings.
`postings.total_postings`	Count of total postings.
`postings.duplicate_postings`	Count of duplicate postings.
`postings.unique_companies`	Count of distinct companies represented in matching postings.
`postings.average_salary`	Average advertised salary.
`postings.max_salary`	Maximum advertised salary.
`postings.median_salary`	Median advertised salary.
`postings.median_posting_duration`	Median posting duration.
`postings.min_salary`	Minimum advertised salary.
`postings.posting_intensity`	Posting intensity metric.
`postings.salary`	Salary domain metric for distributions.
`postings.posting_duration`	Posting duration domain metric for distributions.
`postings.min_years_experience`	Minimum years of experience domain metric for distributions.
`postings.max_years_experience`	Maximum years of experience domain metric for distributions.

The list tool also supports selected posting fields such as posting ID, posted date, expired date, body, city name, company name, raw title, URL, active URLs, score, and salary.

For metric definitions and usage guidance, see Metrics.

Supported MCP Tools

Tool	Supported
`count`	Yes
`distribution`	Yes
`estimate`	Yes
`list`	Yes
`timeseries`	Yes
`top`	Yes