The ROI question
Data science can have strong ROI if your path builds statistics, SQL, Python, modeling, evaluation, domain depth, and a real route into interviews. It gets weaker if you pay for a certificate that produces a few polished notebooks but no proof you can reason about data leakage, weak labels, experiment design, or whether a model should exist.
The financial decision should include training cost, lost income, job-search time, local salary bands, portfolio quality, AI pressure, and whether the first job has a path upward. A cheap course with no hiring signal can be expensive if it delays a stronger path. A degree can be overpriced if you only need analyst-level proof.
Pay lanes to compare
Model the lane, not the title. A reporting analyst, product analyst, analytics engineer, data scientist, and ML engineer may all sit near the same data organization while living in different labor markets. The pay improves when your work is closer to revenue, risk, product decisions, platform reliability, or scarce technical depth.
Reporting and BI
Recurring dashboards, operational metrics, extracts, executive reporting, and self-serve views. Good entry point; ceiling depends on whether the role grows beyond requests.
Product analytics
Funnels, experiments, retention, pricing, adoption, customer behavior, and product decisions. Stronger pay when the company uses analysis to make bets.
Analytics engineering
Modeled data, transformations, semantic layers, metric definitions, dbt-style workflows, and the trusted foundation other analysts use.
Data science and ML
Forecasting, experiments, prediction, ranking, fraud, recommendations, optimization, and model evaluation. Higher ceiling, but a higher proof bar.
Salary questions to ask in interviews
Ask what decisions the role owns, how success is measured, who reviews the work, how much of the week is recurring reporting, whether the team has data engineering support, and what the promotion path requires. For analyst roles, ask whether people move into senior analytics, analytics engineering, product analytics, or management. For scientist roles, ask whether models ship, who owns evaluation, and whether the title is really modeling work or dashboard work with a better label.
The answer tells you whether the role has compounding skill value. A lower first salary can be fine if it builds SQL depth, data-modeling judgment, stakeholder trust, and decision proximity. A higher title can be a trap if the work is shallow and does not make you more valuable two years later.
Sources and methodology
O*NET Database 30.3Occupation descriptions, alternate titles, work context, work activities, and education signals.
BLS OEWS May 2025National wage estimates, percentile pay, mean pay, and employment estimates by SOC group.
BLS Employment Projections2024 to 2034 projected employment, growth, annual openings, entry education, experience, and training.
BLS OOH profileOfficial Occupational Outlook Handbook context for the matched career family.
Career Dish adds fit scores, workload metrics, AI exposure estimates, and interview-style guide scenes on top of public datasets. Those interpretive layers are meant to make the data scannable, not to replace official licensing or school-specific research.