Play data scientist: stats plus software, ride the analytics boom
People comfortable with both statistics and code who want to build models and analysis pipelines, with a shorter path than a PhD-track research role.
First dollar: Entry analyst or DS roles are reachable in about 1-2 years with the right portfolio; the strongest offers cluster around bachelor's-plus-experience or a master's.
Exact steps. Ship the smallest unit of paid value before you gold-plate.
Python (pandas, scikit-learn), SQL, statistics through regression and hypothesis testing, and one visualization tool.
End-to-end projects, ingest, clean, model, ship a result, beat Kaggle-only portfolios. Publish them on GitHub.
Data analyst or junior DS role to build production experience; a master's helps but isn't mandatory with a strong portfolio.
Employers promote people who can tie a model to revenue, cost, or retention, not just an accuracy score.
MLOps/ML engineering tracks toward big-tech comp; management track toward analytics leadership.
Related decks if this one is working or if you need a stronger wincon.
What you do on paper: Collect, model, and analyze large datasets to extract insights and build predictive systems. Fast-growing mathematical-science occupation feeding the AI/analytics boom.
Frequency: ~262,440 employed (BLS OEWS, 2025)