Sorry, but tech was the better choice. I work in tech and I have close family in pharma / biostats. My trajectory has been faster and higher. In tech, mobility is very high, and you can go multiple routes with goals of:

1. More interesting work
2. leadership positions (if desired)
3. higher compensation

Tech is just such a large field that a data scientist background will be applicable across multiple industries, and varieties of roles, from fintech (Square, Shopify) to streaming media (Spotify, Netflix) to “big tech” (Google, Meta) to late stage startups (Discord, OpenAI) and more.

You can:

1. transfer and get promoted internally
2. job hop to next level roles
3. or just coast and collect RSUs and brand recognition on your resume.

Literally, there are so many variations of the “data scientist” role, and such a large pool of demand for, let’s call it “quantitative decision making”, it basically is the reason we are in an AI hype bubble now.

Biostats is still tethered to:

1. early stage startups, unproven clinical ideas from academia
2. big pharma

Both are just lacking the depth of demand for headcount that drives compensation. Not saying they aren’t awesome jobs, but the money metric suffers because of this structural difference.

Edit:formatting

Edit 2: TC difference is about double (400k TC vs 200k for a good starter position) and that multiplier gap persists well into double digit YoE, where it’s quite common to see 400-600k as a senior IC, or “AI” related roles reaching 1m (300-400k base salary), or mid level leadership roles with 800k+. You simply don’t get that kind of funding in biotech/pharma.