Using NLP pattern extraction and A/B testing methodology to outperform the 5-11% industry average by 3x.
In a 2024-2025 job market flooded with applicants and AI-generated resumes, standing out became nearly impossible. Industry data shows response rates have dropped to 5-11% for most applicants.
I built JobTrack to apply software engineering principles to my own job search. Instead of guessing, I would measure and optimize.
LinkedIn import automation to capture every application with metadata
Pattern extraction from job descriptions to identify keyword requirements
Real-time metrics on what application approaches got callbacks
Used NLP to analyze job postings and extract the actual keywords and phrases that correlated with callbacks. Built a model to score my resume against each posting.
Tested different resume versions, cover letter approaches, and application timing. Tracked which variables actually moved the needle on response rates.
Built a visual pipeline to see where applications stalled. This revealed that follow-up timing was the #1 factor in converting initial responses to interviews.
Just tracking applications made me more intentional about each one
3-5 day follow-ups had 2x the conversion rate of 7+ day follow-ups
Targeted applications had better rates, but you still need 100+ attempts
Applications with extracted keywords had measurably higher response rates
The best products solve your own problems - you understand the pain deeply