Introduction Job titles are linguistic riddles for computers. A single role might be called “Financial Planning and Reporting Analyst” (formal), “FP&A Wizard” (start-up flair), or “Numbers Guru – Budgets & Forecasts” (creative but useless for algorithms). When I began mapping these titles to the Lightcast Occupation Taxonomy—a structured framework of 1,800+ standardized roles—I quickly realized that job titles prioritize human appeal over machine readability. This first post explores my journey to automate classification, the pitfalls of rule-based systems, and why even simple titles defy machines. The Problem: Why Job Titles Break Machines The Cost of Ambiguity Job seekers miss roles due to mismatched keywords. Recruiters struggle to benchmark salaries or skills. Analysts waste hours untangling titles like “Interim Senior FP&A Analyst – Insurance” (Is this finance? Insurance? Consulting?). Introducing the Lightcast Taxonomy The Lightcast Occupation Taxonomy (LOT) is a global standard for classifying jobs into a hierarchical framework: Career Areas: Broad domains like Healthcare or Technology. Occupation Groups: Subdomains like Data Science or Financial Planning. Occupations: Granular roles…