Data Analyst / Scientist career ladder: levels, titles, and criteria
Career levels, titles, scope, and promotion criteria for data analyst / scientist roles — from entry level to senior leadership.
data analyst career ladder — quick overview
A data career ladder typically has two tracks: analytics (Data Analyst → Senior Analyst → Lead Analyst → Principal Analyst → Staff Analytics Engineer) and data science/ML (Data Scientist → Senior DS → Lead DS → Principal DS). Management track: Analytics Manager → Director of Analytics → VP of Data. Most orgs have IC paths to Staff/Principal level.
Data Analyst / Scientist IC career levels
| Level | Title | Scope | Key differentiator | Typical YoE |
|---|---|---|---|---|
| L1 | Data Analyst | Assigned analyses; defined questions | Writes SQL queries; builds dashboards; answers defined questions from business stakeholders | 0–2 |
| L2 | Senior Data Analyst | Owns analytics for a business area | Independently scopes analyses; defines metrics; proactively identifies problems in the data | 2–5 |
| L3 | Lead / Staff Data Analyst | Analytics strategy for a domain; cross-team data standards | Defines measurement framework; mentors analysts; builds scalable data pipelines and models | 5–8 |
| L4 | Data Scientist | Statistical modeling; ML features | Builds predictive models; runs A/B tests; translates model outputs into business decisions | 2–6 |
| L5 | Senior Data Scientist | Complex ML systems; research | Designs ML experiments; builds production models; mentors junior scientists | 5–9 |
| L6 | Principal Data Scientist / ML Engineer | Company-wide ML strategy | Technical leader for ML infrastructure and advanced modeling; recognized expert | 9+ |
Common questions
Frequently asked questions
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