Salary Benchmarking and Negotiation

Lightcast data can get answers to always evolving salary and compensation questions. We also have a separate guide to help with Understanding Compensation and Wages. Some of those most popular methods for this information is:

• Standard Occupational Classification (SOC) Level Compensation Insights
• Get Advertised Wages for a Given Lightcast Occupational Taxonomy (LOT)
• Market Salary for a Role
• National Salary Metrics for Lightcast Occupational Taxonomy (LOT)
• National Salary Metrics for Skills

Standard Occupational Classification (SOC) Level Compensation Insights

Description:

In this example we show the process of using a raw title and normalizing it to the government SOC, and then using that SOC to find related salaries.

APIs Used:

• Authentication
• Classification - Normalize the raw title to the SOC
• [Compensation] - Using the SOC to see related compensation

Python Example:

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  SOC Level Compensation Insights

  Open Recipe

Get Advertised Wages for a Given Lightcast Occupational Taxonomy (LOT)

Description

In this example we leverage Lightcast Job Postings data to find related salaries based on the Lightcast Occupation Taxonomy (LOT) against the raw title provided.

APIs Used:

• Authentication
• Classification - Normalize the raw title to the Lightcast Occupational Taxonomy (LOT)
• Job Postings - Find the advertised salaries for the related LOT

Python Example:

Market Salary for a Role

Description:

In this example we use raw job titles, normalize them to the Lightcast Occupational Taxonomy (LOT), and then use our Market Salary API for related salary information.

APIs Used:

• Authentication
• Classification - Normalize the raw job title to the Lightcast Occupational Taxonomy (LOT)
• [Market Salary] - Identify the market salary against the related LOT

Python Example:

National Salary Metrics for Lightcast Occupational Taxonomy (LOT)

Description:

In this example we use raw titles to discover salary percentages given a certain title.

APIs Used:

• Authentication
• Classification - Normalize the raw skills to the Lightcast Occupational Taxonomy (LOT)
• [Market Salary] - Identify the market salary percentiles against the related LOT

Python Example:

National Salary Metrics for Skills

Description:

In this example we normalize skills from raw skills to discover salary percentages against them.

APIs Used:

• Authentication
• Classification - Map a raw skill to a standardized skill
• [Market Salary] - Identify the market salary percentiles for the given skill

Python Example: