Software engineer vs data scientist salary?

In the Bay Area with 3 years experience, you'll likely find data science roles offering a slightly higher base salary initially, maybe 10-15% more. However, don't just look at the starting point. Consider the long-term career growth. Software engineering roles, especially in specialized areas like backend or DevOps, can quickly catch up and even surpass data science salaries, particularly as you move into leadership or principal engineer positions. Data science can plateau if you don't continually upskill in areas like machine learning engineering or move into management. It really boils down to your interests and continuous learning, but pure comp wise, both have huge potential. I'd say do what you like, because it all pans out in the end. Just be prepared to constantly be learning new stuff. That is all.

Alright, let's break down the software engineer vs. data scientist salary question. Generally speaking, both roles are well-compensated, but there are nuances. Entry-level salaries can be pretty similar, but as you gain experience, the divergence can become more noticeable. Data science roles often command a premium, especially if you're working with cutting-edge technologies like machine learning or AI. However, that also means that you need pretty specialized skills to get there. Location matters a ton. San Francisco, New York, and Seattle will generally offer higher salaries than, say, Austin or Atlanta, but the cost of living is also a factor. A $200k salary in San Francisco might feel like $150k somewhere else. I would recommend looking at sites like Glassdoor, Levels.fyi, or Built In to get a better idea of the salary ranges for specific companies and locations. Don't just focus on the money, though. Think about which role aligns better with your interests and skills. A higher salary won't make you happy if you hate your job. Both fields have great career growth potential, so consider what you enjoy doing first and foremost. Good luck!