The data science role inside a FAANG company is often described as a silo, and the frustration is familiar to anyone who has felt the ceiling above them. A working data scientist watches applied scientists and machine learning engineers take on the more technically demanding work, while the path to those roles is gated by leetcode rounds that feel disconnected from day-to-day problem solving. The compensation gap is real, and so is the narrowing of the data science function as AI pushes more of the work into specialized engineering tracks. This is not a complaint about unfairness; it is a practical observation about where the industry is heading.
The leetcode barrier is a genuine obstacle, but it is also a solvable one. Dynamic programming and algorithm drills feel daunting because they are not part of the daily toolkit for most data scientists. Yet the question is not whether the interview process should change. They are asking whether the switch is worth the effort, and the answer is yes, if the goal is ownership of models and the compensation that comes with it. The move from data science to applied science is not about abandoning analytical thinking; it is about adding a layer of engineering rigor that unlocks a different tier of work. The 150 to 200 thousand dollar difference is not a rounding error, and it reflects the market's willingness to pay for people who can both formulate a problem and ship a solution.
What stands out is a clear-eyed view of the trade-off. They are not romanticizing the role or pretending that leetcode is a meaningful measure of applied skill. They are acknowledging the friction and still leaning toward the switch because the alternative, staying in a silo while the AI push reshapes the field, feels riskier. That is the right instinct. The data science title is not disappearing, but it is being redefined, and the people who can build and deploy models will have more leverage than those who are asked to stay one step removed from production. The note about AI narrowing the DS function is not speculative; it is already happening in how teams are staffed and where budgets go.
The practical takeaway is straightforward: treat leetcode as a skill to acquire, not an identity to adopt. The author already has the hard part, which is understanding the business problem and the data. What they need is the interview prep and the software engineering fluency to pass the bar. That is a finite investment with a clear return, and it is far more actionable than waiting for the industry to change its hiring practices. If the goal is to do the applied work and earn the comp that comes with it, the path is not mysterious. It is just uncomfortable. And for a data scientist who has already navigated the complexity of FAANG, that discomfort is not a reason to stay put, it is the cost of entry.