Search papers, labs, and topics across Lattice.
This study investigates how candidates for ML engineering roles articulate their soft skills in CVs, addressing a gap in existing research that primarily focuses on employer demands. By analyzing a balanced corpus of 300 curated CVs with an LLM-based pipeline validated against human annotations, the authors identify that candidates predominantly express soft skills through narrative rather than keywords, particularly for competencies like leadership and mentoring. The findings reveal significant differences in how soft skills are disclosed based on role and seniority, with implications for improving CV screening processes in hiring practices.
Candidates reveal soft skills through narrative in CVs three times more often than through keywords, challenging traditional screening methods.
Soft skills shape collaboration among ML engineers, data scientists, and software engineers building ML-enabled systems, yet what we know about them comes almost entirely from the demand side. Job advertisements, surveys, and hiring manager interviews capture what employers ask for. How candidates themselves articulate these competencies has not been studied, and existing CV-mining work is both keyword-based, so it cannot see skills conveyed through narrative, and descriptive, reporting frequency rankings without testing whether group differences exceed sampling variation. We close both gaps. Using a balanced corpus of 300 curated CVs spanning the three roles, we extract explicitly listed and implicitly narrated soft skills with an LLM-based pipeline validated against a human-annotated ground truth, a distinction that existing extractors were not designed to make. We then convert the demand-side literature's claims into 13 falsifiable hypotheses about role signatures, seniority progression, and disclosure style, and test them with effect sizes under family-wise error control, so that candidate-side data can corroborate or contradict the demand-side account rather than merely illustrate it. Eleven hypotheses are supported, one partially, and one refuted. Candidates disclose soft skills through narrative rather than keyword lists by roughly three to one, and most so for the competencies employers value most: leadership, coordination, and mentoring (88-96% narrative). Seniority nearly triples the odds of articulating leadership. That competency, assumed universal in prior work, is articulated by software engineers at half the rate of their peers. Technical candidates do articulate soft skills, but a keyword-based screening systematically misses them.
