pre-filter-candidates

To pre-filter candidates from an enriched CSV, you run a workflow that matches first and last names against a past_candidates.csv file and excludes current companies found in a blacklist. This produces a clean filtered_candidates.csv subset ready for scoring.

CSV data cleaning for duplicate applicant removal involves matching candidate first_name and last_name fields against historical records in a past_candidates.csv file. This pre-filtering process ensures previously processed individuals are excluded from the active candidate pipeline.

Yes, candidate CSV filtering can apply optional rules to flag top-university graduates and detect job hoppers. These pre-filter checks run alongside blacklist matching and not-relevant company exclusion before generating the final output file.

No specific dependencies or external components are required to run the pre-filter candidate workflow. It operates directly on an enriched candidate CSV and a client folder containing filter lists to output the filtered results.

The best way to exclude not-relevant or target companies from candidate records is applying optional blacklist filters during the CSV pre-filtering stage. This removes unwanted employer matches and outputs a summary of all exclusions per rule.

Candidate CSV filtering fails to remove past applicants when first_name and last_name fields do not exactly match records in past_candidates.csv. Accurate pre-filtering requires clean, consistently formatted name columns across both input files.