A data scientist who landed $100k+ offers at companies like Gousto and DoorDash says he applied to 400 roles before his first hire. He now shares mistakes to avoid, focusing on core skills over trendy tools. He advises new candidates to skip learning AWS or Docker for interviews, instead mastering probability, basic algorithms, and LeetCode problems.

The author criticizes a "scatter gun" application approach, advocating a targeted strategy. He suggests finding roles that match a university thesis, a relevant side project, or a less competitive location. Starting at smaller companies before aiming for FAANG is a more sustainable path. A generic resume is a major flaw; it should be clean, one-page, and lead with financial impact metrics.

Tailoring each resume for the job description is critical to pass ATS filters. The most powerful tool is a referral, which makes candidates four times more likely to be hired. He recommends using LinkedIn to connect with 50 people weekly at target companies, building rapport before asking. Following up after applications completes the disciplined, personalized approach needed in today's market.