International Journal of Science, Engineering and TechnologyAuthors: Priyanka Narang, Tathya Sharma, Aditi Kashyap, Mamta
Abstract: Traditional Applicant Tracking Systems (ATS) and open source resume analyzers rely on rigid regex rules and hardcoded penalty schemes. As a result, they are brittle, and candidates are penalized for using different but equivalent terminology. These systems often fail to reflect how skills and roles actually appear in real industry data. This paper presented a fully data-driven multi-model machine learning pipeline that replaced arbitrary scoring rules with seven coordinated models. A Multi-Stage Waterfall Extraction model combined a custom spaCy named entity recognition system with SBERT-based cosine similarity to overcome sparse and noisy annotations. An XGBoost-driven linear regression severity engine and an implicit ontology inference module revealed invisible ATS filters rooted in real hiring expectations. A deterministic Explainable AI layer then grounded all feedback in the candidate's own verified text, removing operational hallucinations. The paper reported empirical results including the Soft Skill Anomaly, which challenges common developer assumptions about soft skills in ATS design.
DOI: http://doi.org/10.5281/zenodo.21772999
