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Data Analyst
Data analysts live between data and business decisions: Structured Query Language (SQL), spreadsheets, dashboards, metric definitions, data cleaning, and explanations for teams that need to act. AI reaches routine queries, charts, summaries, and first-pass anomaly explanations, so simple report production is exposed. The work holds up better when the analyst knows what the metric means, where the data is messy, and how a finding could be misread. Federal labor data does not isolate this job; the workforce and openings numbers here come from a broader data-science-and-analytics occupation with strong growth.
The variable to examine is whether a role is report production or decision support. Business intelligence (BI) analyst roles embedded in sales, finance, operations, or healthcare can reward context and stakeholder judgment. Roles that only refresh dashboards, write routine SQL, or summarize obvious trends are easier for AI tools to compress. Compare programs and internships on whether they teach data quality, metric design, and business questions, not just dashboard software. Data scientist is the modeling and machine-learning alternative if you want heavier statistics and model work.
The best-fit data analysts usually like asking why a number changed, not just making the chart prettier for a meeting. They can sit with ambiguity, check whether a dataset is trustworthy, and explain tradeoffs to people who do not speak data all day yet. The underexpected demand is social: many findings die because the analyst cannot translate them into a decision, a question, or a next test the business actually understands.