Talent Acquisition Strategy Optimization

HR and talent acquisition teams can leverage Lightcast data to identify emerging skills and occupations in high demand within specific regions or industries. This information helps organizations refine their recruitment strategies and ensure they are hiring talent with the most relevant skills for their business needs. Some common uses cases below include:

• Location of Employee Supply
• Distinguishing Skills for Lightcast Occupational Taxonomy
• Role Searching for Similar Roles
• Find Feeder Jobs for a Given Occupation
• Market Salary for a Given Role
• Compensation Insight by Standard Occupational Classification (S.O.C.)

Location of Employee Supply

Description:

In this example we classify raw job titles to the Lightcast Occupational Taxonomy (LOT) and then use that LOT to get supply data from our global profiles data set.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• Profiles - Our employee profile data API

Python Example:

Distinguishing Skills for Lightcast Occupational Taxonomy

Description:

In this example we use the Classification API to convert a raw title to the Lightcast Occupational Taxonomy (LOT), and then use that title to pull data from our DDN (defining, distinguising, and necessary skills) API.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• DDN API

Python Example:

Role Searching for Similar Roles

Description:

In this example we show the process of normalizing a raw title to the Lightcast Occupational Taxonomy (LOT), and then using that title to find similar roles against our similar roles model API.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• Similarity API

Python Example:

Find Feeder Jobs for a Given Occupation

Description:

In this example we normalize raw titles to the Lightcast Occupational Taxonomy (LOT), and use that title to find advancement jobs with a similar career trajectory.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• Career Pathways API

Python Example:

Market Salary for a Given Role

Description:

In this example we normalize raw titles to the Lightcast Occupational Taxonomy (LOT), and use that title to uncover salary percentiles for that role.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• Market Salary

Python Example:

Compensation Insight by Standard Occupational Classification (S.O.C.)

Description:

In this example we normalize raw titles to the Standard Occupational Classification (S.O.C.) and use that S.O.C. to uncover estimated compensation for that role.

APIs Used:

• Authentication
• Classification - To classify the raw title to the Lightcast Occupational Taxonomy (LOT)
• Compensation API

Python Example: