DESCRIPTION

The purpose of this use case is to increase the number of blue-collar profiles in our dataset. Currently, a majority of the user profiles we have are white-collar focused, which limits our ability to provide insights and analysis into blue-collar industries and occupations. The goal is to increase the number of blue-collar profiles in our dataset to provide a more comprehensive view of the job market and support a wider range of research and analysis.

BUSINESS GOALS

• Increase the number of blue-collar profiles in our dataset to support more comprehensive research and analysis.

• Enhance the accuracy and completeness of our dataset to improve user experience and satisfaction.

• Establish ourselves as a leading provider of job market insights and analysis.

END USERS

• Consulting: Use our dataset to conduct research and analysis on the job market.

• Talent: Use our dataset to inform hiring and recruitment strategies.

• Education: Use our dataset to gain insights into job market trends and opportunities.

INPUTS

• LENS.

• Data storage and management infrastructure.

PROCESS

1. Use resumes collected from our approved partners with LENS technology.

2. Create and stage “new” profiles from resumes.

3. Compare and identify “new” profiles to existing ones to determine if there are duplicates between the datasets. 

4. Identify profile information associated with blue-collar professions, including details like industry, title, location, etc.

5. Store and merge the “new” profiles into our main dataset.

6. Develop and implement data quality checks and validation processes to ensure accuracy and completeness of profile data with this new process.

OUTCOMES

• Increased number of blue-collar profiles in our dataset.

• Enhanced completeness of our dataset.

• Increased user engagement and retention.

METRICS

• Number and proportion of blue-collar profiles in our dataset.

• Number of existing resumes->profiles that are existing profiles to date.

• Completeness and accuracy of user profile data.

ASSUMPTIONS

• Job posting data sources that are likely to contain blue-collar job listings can be identified and obtained.

• Machine learning models can accurately and reliably classify job postings as blue-collar or white-collar.

• User profile data associated with blue-collar job postings is available and can be accurately extracted and stored.

RISKS

• Bogus/phony resumes may not be of actual people.

• Older resumes would mean out-of-date profiles.

• Metadata associated with blue-collar profiles may not be available or may be incomplete or inaccurate.

• Data quality checks and validation processes may not catch all errors or issues in the user profile data.