Compare · Updated 16 September 2026
Actuary vs Data Scientist: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Data Scientist pays more at mid-career: a median of $140,000 against $105,000 for Actuary, about 33% higher. Actuary is faster to enter: the quickest verified route takes about 12 months versus 12 for Data Scientist. Job growth favours Data Scientist (35% projected over ten years, BLS 2025-35, versus 9%).
| Actuary | Data Scientist | |
|---|---|---|
| Entry median | $70,000 | $110,000 |
| Mid-career median | $105,000 | $140,000 |
| Senior median | $160,000 | $180,000 |
| Top end | $215,100 | $199,130 |
| Roadmap hours | 4,880 | 890 |
| Fastest way in | Pass two exams, then apply cold (12 mo) | Domain expert converting (12 mo) |
| Cheapest way in | $600 | $0 |
| Time to first job | 9–15 months | 18–36 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, but a quantitative master's or PhD is a hard filter in pharma, biostatistics, clinical research and quantitative finance; product analytics teams at technology companies and most mid-size employers are where demonstrated skill substitutes for the credential. |
| 10-year growth | 9% | 35% |
| Openings per year | 1,500 | 24,800 |
| Automation exposure | low | medium |
| Key certification | Associate of the Society of Actuaries (ASA) | IBM Data Science 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) | Python, SQL (Snowflake, BigQuery, Databricks, Redshift), Jupyter / VS Code, scikit-learn, XGBoost / LightGBM |
Salary figures checked August 2026 (Actuary) and September 2026 (Data Scientist). 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 Scientist does
A Data Scientist is a specialist who estimates what would happen rather than reporting what already did, by designing and reading out A/B tests, building forecasting and propensity models, and using causal inference when a clean experiment is impossible.
A data scientist sits between the analyst and the engineer. Where an analyst reports what happened, a data scientist estimates what would happen: designs and reads out A/B tests, builds forecasting and propensity models, does causal inference when a clean experiment is impossible, and builds the metric frameworks the rest of the company argues over. In practice the job splits into two archetypes. Product / decision science - heavy on SQL, experimentation, causal methods and stakeholder influence, common at consumer tech, marketplaces and fintech. Modelling / applied science - heavy on Python, scikit-learn, feature engineering and model evaluation, common in insurance, credit risk, healthcare, pricing and demand forecasting. Read the job description carefully, because the interview loops for the two are almost entirely different.
- BLS median of $120,230 with 35% projected growth to 2035 - one of the strongest wage-and-growth combinations in the US labour market.
- Genuine intellectual variety: the same week can involve causal inference, forecasting and a metric argument.
- Portable across industries; the statistics travels even when the domain does not.
How to choose between Actuary and Data Scientist
- 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 Scientist if the route that produces the most career-changer hires is analyst first and Data Scientist second: 18 to 30 months of production experience substitutes for a graduate degree in a way no certificate does, and you are paid throughout. Going direct from zero means competing with master's graduates for the same junior slot.
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 machine learning engineer and AI engineer, which have absorbed most of the production modelling work and pay more, plus analytics and data science management. Both engineering moves raise the software bar (testing, services, deployment) rather than the statistics bar, and neither requires a graduate degree.
Actuary vs Data Scientist FAQ
Which pays more, Actuary or Data Scientist?
At mid-career the median is $105,000 for a Actuary and $140,000 for a Data Scientist; at senior level $160,000 versus $180,000. Entry medians are $70,000 and $110,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Actuary or a Data Scientist?
The quickest verified route into Actuary is Pass two exams, then apply cold at about 12 months; for Data Scientist it is Domain expert converting at about 12 months. Our full roadmaps run 4,880 and 890 study hours respectively.
Which is harder to automate, Actuary or Data Scientist?
We rate automation exposure low for Actuary and medium for Data Scientist. 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 already do exploratory analysis, boilerplate feature engineering and first-draft modelling code well, which erodes the junior end of Data Scientist work; the US Bureau of Labor Statistics cites the integration of AI into business workflows as a reason the occupation is projected to grow 35 percent to 2035. Experiment design, causal inference, spotting an artefact and persuading an organisation to act are not close to automated.
Do I need a certification for Actuary or Data Scientist?
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 or expected for a Data Scientist job, and none of them substitutes for a degree or production experience. The most useful item on the list is the Machine Learning Specialization from Stanford Online and DeepLearning.AI at $49 a month for about two months and 95 hours, which is a curriculum rather than a credential. The Databricks Certified Machine Learning Associate at $200 is the only proctored exam here and it only matters if your target employers run Databricks; the DataCamp certifications carry little weight with hiring managers.