When hiring for technical positions, people often use high-precision heuristics conditional on relevance to decide who to interview. Otherwise, the interviewing burden would be too much. The advent of GPT makes use of high-precision heuristics yet more important as interviewing and resumes are less trustworthy now. The heuristics people often rely on are ~ network, fancy pants school and company, and international student (for junior applicants). But this likely leads to bias against certain groups, etc. There are at least three potential solutions that improve upon the status quo:
- Use test scores from exams that are widely taken that are correlated with performance (after confirming the relationship), e.g., IQ, SAT, GRE, and use that to filter.
- Use testing companies to offer proctored exams in topical areas. Companies like Karat are well placed to offer such testing. And working with companies like Coursera, which have strong incentives to make their certifications meaningful, may be a good idea. The larger idea has data behind it. Advanced software certifications offered by major companies are widely viewed as good signals of competence.
- Create a paid show-me-your-work period. The solution is the least attractive as it is expensive for the company and for most applicants (except for fresh graduates or unemployed people).
p.s. Many of the job postings are too generically worded and plausibly cause more unqualified people to apply. Making job descriptions more precise, e.g., “proficiency in pandas,” may help.