Compare · Updated 16 September 2026
Actuary vs Data Analyst: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Actuary pays more at mid-career: a median of $105,000 against $88,000 for Data Analyst, about 19% higher. Data Analyst is faster to enter: the quickest verified route takes about 6 months versus 12 for Actuary. Job growth favours Data Analyst (12% projected over ten years, BLS 2025-35, versus 9%).
| Actuary | Data Analyst | |
|---|---|---|
| Entry median | $70,000 | $68,000 |
| Mid-career median | $105,000 | $88,000 |
| Senior median | $160,000 | $115,000 |
| Top end | $215,100 | $154,571 |
| Roadmap hours | 4,880 | 630 |
| Fastest way in | Pass two exams, then apply cold (12 mo) | Analytics bootcamp (6 mo) |
| Cheapest way in | $600 | $0 |
| Time to first job | 9–15 months | 9–18 months |
| Degree | A bachelor's degree in any quantitative subject is the practical floor and the US Bureau of Labor Statistics lists a bachelor's degree as the typical entry-level education for Actuaries, but no actuarial science major is required and employers screen on exams passed, not on the name of the degree. | No degree is legally required to work as a Data Analyst and a portfolio genuinely substitutes for one at small and mid-size employers, but most large-company postings still list a bachelor's degree as a preference. |
| 10-year growth | 9% | 12% |
| Openings per year | 1,500 | 7,500 |
| Automation exposure | low | medium |
| Key certification | Associate of the Society of Actuaries (ASA) | Google Data Analytics Professional Certificate |
| Tools | R (the SOA's Predictive Analytics exam is administered in R), Python (pandas, scikit-learn, statsmodels), Excel and VBA (still the daily workhorse in most actuarial departments), SQL for policy and claims data extraction, Prophet, AXIS, MG-ALFA or GGY ALFA (life and annuity valuation platforms) | SQL (PostgreSQL, Snowflake, BigQuery, SQL Server), Excel / Google Sheets, Power BI, Tableau, Looker / Looker Studio |
Salary figures checked August 2026 (Actuary) and September 2026 (Data Analyst). Sources are listed on each career page.
What a Actuary does
A Actuary is a credentialed professional who prices and reserves for uncertain future events - death, illness, accidents, catastrophes and pension promises - using probability and financial mathematics, and who earns the credential by passing professional exams rather than by taking a degree.
An actuary puts a price on uncertain future events. In life and annuity work that means mortality, longevity and policyholder behavior; in property and casualty it means claim frequency and severity for auto, homeowners, workers' compensation and commercial lines; in health it means medical trend and risk adjustment; in pensions it means funding a promise decades out. The output is concrete: a rate filing, a reserve estimate on a balance sheet, an economic capital number, a valuation certificate signed by a credentialed actuary and relied on by a regulator.
- Pay rises mechanically with exams passed - one of the clearest, most transparent compensation ladders in any profession.
- Employers pay for exams, study materials and give 80-120 paid study hours per sitting, plus a cash bonus on each pass.
- Excellent work-life balance outside of study time: 40-45 hour weeks are the norm in insurance roles.
What a Data Analyst does
A Data Analyst is a person who turns a company's raw database into answers a manager can act on, using SQL, spreadsheets and a dashboard tool such as Power BI or Tableau.
A data analyst turns a messy company database into answers. The core loop is: a stakeholder asks something vague ("why did signups drop in July?"), you translate it into a query, pull the data with SQL, clean and check it, build a chart or a Power BI/Tableau dashboard, and write two paragraphs that say what happened and what to do. Most of the job is SQL and communication; the modelling is usually descriptive statistics, cohorts, funnels and the occasional A/B test readout. Titles vary a lot: business analyst, BI analyst, marketing analyst, product analyst, revenue operations analyst and healthcare data analyst are all the same skill set pointed at a different domain.
- Lowest barrier to entry of any six-figure-track data role: no degree requirement, no calculus, and a portfolio genuinely substitutes for credentials.
- Skills transfer across every industry, so you can follow the domain you find interesting or the one that pays.
- Mostly predictable hours - 40-45 a week outside month-end close and board-deck season.
How to choose between Actuary and Data Analyst
- Pick Actuary if most career-changer hires come from one of two routes: passing Exam P and Exam FM and applying cold to actuarial analyst roles, or transferring internally from an underwriting, claims, finance or data seat at an insurer, which is the highest-probability route of all because the employer already knows you and starts paying for your exams the day you move.
- Pick Data Analyst if internal transfer from a non-analyst job at your current employer produces more career-changer hires than cold applications do, because it converts domain knowledge you already have into the thing employers are actually short of; the certificate-plus-portfolio route works too, but it is the slowest of the five, at about 14 months and 150 to 300 applications.
The natural moves out of actuarial work are into quantitative analysis, data science, risk management and insurance product or pricing leadership. Pay is comparable or higher in quantitative finance but the entry filter switches from exams to pedigree and interviews, and you give up the exam ladder's unusual property: a portable, merit-based credential that raises your salary on a published schedule regardless of who your employer is. The natural next moves are analytics engineer, which pays more for deeper SQL, dbt and pipeline ownership, and Data Scientist, where the US Bureau of Labor Statistics median is $120,230 against $88,940 for the operations research analyst code that BLS uses for analyst work, and where a quantitative master's degree is a common filter.
Actuary vs Data Analyst FAQ
Which pays more, Actuary or Data Analyst?
At mid-career the median is $105,000 for a Actuary and $88,000 for a Data Analyst; at senior level $160,000 versus $115,000. Entry medians are $70,000 and $68,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Actuary or a Data Analyst?
The quickest verified route into Actuary is Pass two exams, then apply cold at about 12 months; for Data Analyst it is Analytics bootcamp at about 6 months. Our full roadmaps run 4,880 and 630 study hours respectively.
Which is harder to automate, Actuary or Data Analyst?
We rate automation exposure low for Actuary and medium for Data Analyst. Automation has absorbed the manual calculation and now does much of the model-fitting, which is why the Society of Actuaries added Exam PA and Exam ATPA in predictive analytics to the Associate pathway. What does not automate is the signature: statements of actuarial opinion on loss and life reserves must be signed by a qualified actuary under state insurance law and the Actuarial Standards of Practice, and that signature carries personal professional liability. Large language models now write competent SQL and first-draft charts, which compresses the ticket-taking half of Data Analyst work and is one reason Indeed's Hiring Lab counted entry-level postings down 7.5 percent year over year in May 2026. Owning metric definitions, experiments and the stakeholder relationship is what does not automate.
Do I need a certification for Actuary or Data Analyst?
The Society of Actuaries or Casualty Actuarial Society exams are not optional: the credential is the only way into the profession, and there is no degree that substitutes for it. Exam P and Exam FM cost $275 each at 2026 Society of Actuaries rates and are the two that actually decide whether you get interviewed; a full Associate of the Society of Actuaries pathway runs roughly $7,400 to $7,800 in first-pass fees and $9,000 to $12,000 with retakes, almost all of it employer-paid once you are hired. The Validation by Educational Experience credits at $92 per topic are administrative rather than a credential, but candidates routinely finish six exams and then stall because a Validation by Educational Experience topic is outstanding. No certification is required to work as a Data Analyst. The one with genuine screening value is Microsoft's PL-300 (Power BI Data Analyst Associate) at $165 for the exam in the United States and 40 to 90 hours of study, and it is worth having if you are targeting Microsoft-shop employers in corporate, healthcare, insurance or government. The Google Data Analytics Professional Certificate costs about $147 to $294 over three to six months and is worth taking as a curriculum, but it carries little weight as a credential on its own: there is no proctored exam and 3.8 million people have enrolled.