Hey folks, I have a use case around PredictLeads job postings where I'm getting stuck. I want to return Job Postings that match my keyword inclusion list and don't match my exclusion list.
why: I use Linkedin Alerts for job opening signals today, but Linkedin is really bad with a) context/num characters it allows b) "NOT" operators. I want to filter out the noise in open job postings, so I can reduce the manual effort here.
Here's pseudocode.
Included Keywords: Marketing, Data, Digital
Excluded Keywords: Crime, Fraud, Security, Sales, Talent
Based on Company Domain and Included Keywords, look up open roles via PredictLeads. A JSON object returns, which could contain 15 job openings matches for a given company.
1 - If any of the Excluded Keywords are found in Job Title[0], ignore the role entirely and move onto Job Title[1]. If there is no match, move onto step 2.
2 - See if the Job Title[0] matches any of the Included Keywords. If not, see if the Job Description[0] matches any of the Included Keywords.
----- 2A If not, ignore the role entirely and move onto Job Title[1]
----- 2B If a match is found, write down the sentences where that keyword was found and continue parsing the description for more tidbits. Add all of this into an object/array.
3. Do this again for all x results in that company's open job postings
4. Once I have an object with a slimmed down Job Title > Relevant Sentences, I can begin to build out some subject lines/first lines that might be relevant
Problems I'm experiencing:
I can't use the AI integration, since the job posting description itself is far too many characters for Clay's integration to handle (on free plan). I tried using the AI Formula Generator and got closer, but still not there. (maybe I parse this description into 3-4 separate fields and run the AI on all of them independently?)
I'm having trouble with Clay being able to handle nested JSON objects - it feels easy to de-construct an object but I'm not sure if I can construct an object.
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