post https://api.adept-id.com/v2/occupation-classification-async
Standardize a job title it into an <<glossary:O*NET>> occupation (i.e. a category of roles). This endpoint supports the US Bureau of Labor Statistics ONET/SOC taxonomy. This asynchronous endpoint supports standardizing a large number of job titles at once.
Use this endpoint to standardize job titles into an O*NET occupational category. It accepts a job title and optionally an employer name or industry and returns the occupational category for the role. This is an asynchronous version of Occupation Classification, meant to support classifying a large number of job titles at once.
Notes:
- The payload schema for this endpoint is the same as that of Occupation Classification. As with other AdeptID endpoints, this payload can be sent in a compressed format (e.g., using GZIP). It accepts a maximum file size of 15 MB.
- Callers of this endpoint will receive an immediate response containing a
result_set_id
and aresult_set_link
. To retrieve the results, theresult_set_id
must be sent via Result Sets (result_set_link
pre-formats the URL for this step). See Result Sets for additional instructions.
Additional Notes:
- Employer and Industry: This endpoint accepts employer and industry as optional inputs to support disambiguation of generic job titles such as Associate, Engineer or Technician. In these cases, we attempt to determine the industry of the employer and use that to refine the results. It is not necessary to include both employer and industry.
- Occupation Taxonomy: The default occupation taxonomy used for outputs of this endpoint is ONET 2019 Simplified. If you wish to use the full ONET 2019 taxonomy enter
ONET_2019
into the occupation_subset parameter. For more details on supported occupation taxonomies, see the Occupation Taxonomy section of our documentation center. - Match Confidence: The
occupation_match_confidence
value describes how likely each suggested occupation is to be an accurate classification. In cases where it is preferable to deliver no matches over low-quality matches, theoccupation_match_confidence_min
parameter can be used to exclude weak matches. In most cases, 0.6 works as starting point for experimentation.