The engine of the AI boom — turn data into models and decisions. The 4th fastest-growing job in the economy, with a clear on-ramp from analyst roles.
Gold = credential gate. Each turn: more skill → more pay → the next step.
1
Foundations: stats, Python, SQL
Bachelor's in math/CS/stats is typical; a master's is common but not always required.
◆ GMA Academy — data/Python
2
Build a model portfolio
2–3 end-to-end projects on GitHub/Kaggle. Optional: Google Data Analytics, AWS ML certs.
◆ GMA jobs + mentor
3
Enter via an analyst / data role
Data analyst or junior data scientist; many enter from an adjacent analytics job.
4
Put models into production (2–4 yrs)
Own real ML that ships; reach the median and beyond.
5
Specialize into ML / AI engineering
The highest-paid branch — build the AI systems (MLOps, LLMs). A master's/PhD opens research-scientist roles.
The flywheel: each shipped model builds the reputation + skills for a harder, better-paid problem — analyst → data scientist → ML/AI engineer, the hottest ladder in tech.
