Actuary vs Data Scientist Salary 2026

You’re looking at actuary vs data scientist salary numbers because you want the straight answer: which career pays more in 2026, which one is safer, and which one is actually worth the effort. Fair. Both jobs can pay very well, but they reward different skills, different personalities, and very different career paths.

If you’re choosing between actuarial science and data science, the salary headline is not enough. You need to know how pay grows, what happens at senior levels, how exams affect actuarial income, how AI affects data science hiring, and what companies are really paying in the US and Europe.

Let’s break it down in normal human language.

Actuary vs Data Scientist Salary 2026: Quick Answer#

In 2026, data scientists usually have higher starting salaries, especially in tech, fintech, and AI-heavy companies.

Actuaries often catch up later, especially after professional credentials like Associate or Fellow status. A fully credentialed actuary in insurance, consulting, or risk can earn as much as, or more than, many senior data scientists.

Here’s the short version:

Big US employers like Google, Meta, Amazon, Netflix, Stripe, and OpenAI can push senior data scientist compensation above $250k to $400k with stock and bonuses.

Large insurers and consultancies like Aon, Milliman, Munich Re, Swiss Re, Allianz, AXA, Prudential, MetLife, and WTW can pay senior actuaries $180k to $300k+, especially for fellows, managers, and chief actuary tracks.

So if you want the quickest route to a high salary, data science often wins.

If you want a more structured, credential-based career where each exam can move your income up, actuarial science is extremely strong.

What Does an Actuary Actually Do?#

An actuary prices risk.

That sounds boring until you realize risk is everywhere: life insurance, health insurance, pensions, car insurance, climate events, cyber insurance, banking risk, and retirement planning.

Actuaries help companies answer questions like:

1. How much should this insurance policy cost?
2. How likely is a customer to make a claim?
3. How much money do we need in reserves?
4. What happens if interest rates change?
5. How much pension money will be needed in 30 years?
6. How do we price catastrophe, mortality, or health risk?

Actuaries work with math, probability, statistics, finance, regulation, and business judgment. You’ll often use Excel, SQL, Python, R, SAS, actuarial modeling tools, and internal pricing systems.

The classic industries are:

• Life insurance
• Health insurance
• Property and casualty insurance
• Reinsurance
• Pension consulting
• Risk consulting
• Investment and asset management
• Banking risk
• Government agencies

The biggest thing to understand: actuarial careers are tied to exams.

In the US, that usually means the Society of Actuaries, SOA, or Casualty Actuarial Society, CAS. In the UK, it’s the Institute and Faculty of Actuaries, IFoA.

Those exams are hard. Like, “tell your friends you’re busy for the next three months” hard.

But they also give the career a salary ladder that many jobs do not have.

What Does a Data Scientist Actually Do?#

A data scientist uses data to solve business problems.

That can mean building machine learning models, testing product changes, forecasting demand, detecting fraud, segmenting customers, building recommendation systems, or explaining why revenue dropped last Tuesday.

A data scientist may work on:

1. Predictive models
2. Machine learning pipelines
3. A/B testing
4. Customer analytics
5. Product analytics
6. Natural language processing
7. Fraud detection
8. Pricing models
9. Forecasting
10. AI product features

You’ll often use Python, SQL, pandas, scikit-learn, PyTorch, TensorFlow, Spark, dbt, Tableau, Looker, Databricks, Snowflake, and cloud platforms like AWS, Azure, or Google Cloud.

Data scientists work almost everywhere now:

• Big Tech
• Startups
• Banks
• Insurance companies
• Retail
• Healthcare
• Logistics
• Consulting
• Gaming
• SaaS
• Energy
• Government

Unlike actuarial work, data science does not have one clean credential path.

That is good and bad.

Good because you do not need to pass years of exams to earn well. Bad because hiring can be messy, competitive, and full of vague job titles.

One company’s “data scientist” is basically a machine learning engineer. Another company’s data scientist is closer to a business analyst with Python.

Entry-Level Salary: Who Earns More Right After Graduation?#

Data scientists usually earn more at entry level.

In the US, entry-level data scientists in 2026 often land between $85k and $120k, depending on city, degree, company, and technical skills.

At companies like Amazon, Microsoft, Google, Meta, and Apple, new grads in data science or applied science roles can see total compensation above $130k, especially in Seattle, San Francisco, New York, Austin, or Boston.

Entry-level actuaries usually start around $70k to $95k in the US.

If you have one or two actuarial exams passed, a strong internship, and good technical skills, you can be on the higher side. In cities like New York, Chicago, Hartford, Boston, and Philadelphia, starting actuarial salaries can reach the $85k to $100k range.

In Europe, entry-level pay varies more.

Typical early-career numbers:

• Germany: data scientist €55k to €75k, actuary €50k to €70k
• Netherlands: data scientist €50k to €70k, actuary €48k to €68k
• France: data scientist €45k to €65k, actuary €45k to €65k
• Ireland: data scientist €55k to €75k, actuary €50k to €70k
• UK: data scientist £45k to £65k, actuary £40k to £60k
• Switzerland: data scientist CHF 95k to CHF 130k, actuary CHF 90k to CHF 125k

If you want the best starting salary, data science has the edge.

But entry-level salary is only the first lap.

Mid-Career Salary: The Gap Gets More Interesting#

At mid-career, the comparison becomes less obvious.

A data scientist with 4 to 7 years of experience can often earn $120k to $170k in the US. In strong tech companies, fintech, AI startups, and product-led companies, total compensation can hit $180k to $230k.

A mid-career actuary can earn $100k to $145k, but credentials matter a lot.

If you’re an Associate actuary, such as ASA, ACAS, or nearly credentialed, you might be in the $110k to $150k range. If you’re close to Fellow status, your pay can jump quickly.

Mid-career actuarial salaries also depend heavily on track:

• Property and casualty actuaries often earn more than pension actuaries
• Reinsurance can pay very well
• Consulting can pay more, but hours may be tougher
• Health insurance can be stable and lucrative
• Life insurance roles can be steady, especially in large carriers

At this stage, data scientists may still earn more on average, but actuaries have a clearer path to raises.

A mid-level data scientist has to prove impact:

1. Did your model increase revenue?
2. Did your experiment change product strategy?
3. Did your forecast reduce costs?
4. Can you explain complex work to business leaders?
5. Can you ship production-grade analysis or models?

A mid-level actuary has to prove both work impact and exam progress.

That can feel stressful, but it also gives you a visible promotion system. Pass exam, gain credential, increase value.

Senior Salary: Where Both Careers Can Get Very Rich#

Senior pay is where things get spicy.

Senior data scientists in the US usually earn $170k to $250k total compensation. At top tech firms, senior data scientists, applied scientists, research scientists, and machine learning scientists can earn $250k to $400k+.

For example:

• Meta senior data science roles can reach $250k to $350k+ total compensation
• Google senior data scientist or research roles can reach $250k to $400k+
• Amazon applied scientist roles can reach $220k to $350k+
• Netflix senior analytics and ML roles can exceed $300k
• Stripe, Databricks, Airbnb, and Uber can also pay very high packages

Actuaries can also earn serious money.

A fully credentialed Fellow actuary, such as FSA or FCAS, can earn $150k to $250k+ in the US. Senior managers, principals, partners, chief actuaries, and risk leaders can earn $250k to $500k+, especially with bonus.

Examples:

• Milliman consulting actuaries can earn strong six-figure compensation, with high upside for principals
• Aon, WTW, and Mercer senior consultants can move into $180k to $300k+ territory
• Swiss Re, Munich Re, and Hannover Re senior reinsurance actuaries can earn very well
• Large US insurers like MetLife, Prudential, New York Life, The Hartford, Travelers, Liberty Mutual, and Chubb pay senior credentialed actuaries very competitively

Here’s the key difference.

Senior data science compensation often depends on company type and stock.

Senior actuarial compensation often depends on credentials, management responsibility, client responsibility, and regulatory importance.

If you land in Big Tech, data science can beat actuarial pay.

If you become a Fellow actuary in a high-value insurance or consulting niche, actuarial pay is extremely competitive and may be more stable.

Bonus, Stock, and Benefits: Don’t Ignore Total Compensation#

Base salary is only part of the story.

Data scientists at tech companies often receive:

• Annual bonus
• Restricted stock units, RSUs
• Sign-on bonus
• Equity refreshers
• Remote work flexibility
• Learning budget
• Strong parental leave
• Health benefits
• 401(k) match in the US

A data scientist earning a $170k base may have total compensation of $220k once stock and bonus are included.

Actuaries often receive:

• Annual bonus
• Exam raises
• Exam bonuses
• Paid study time
• Professional dues covered
• Pension or retirement benefits
• Strong insurance benefits
• Clear promotion criteria

The paid study time is a big deal.

Some insurance companies give actuarial students 100 to 150 paid study hours per exam. They may also pay exam fees and give raises after each pass.

If you’re comparing only salary, you might miss that actuarial employers are literally paying you to study toward a credential that increases your income.

That’s rare.

Work-Life Balance: Which Career Is Better?#

Actuarial work often has better work-life balance than data science, but not always.

Traditional insurance actuarial roles can be relatively predictable. You may work heavier hours during quarter-end, year-end, pricing cycles, valuation deadlines, or regulatory filings.

Consulting actuaries usually work more hours, especially at firms like Milliman, Aon, WTW, Mercer, and Deloitte. Client deadlines can get intense.

Data science work-life balance depends heavily on company culture.

At a stable company, analytics data science can be a normal 40 to 45 hour week. At a fast startup or Big Tech team with product pressure, it can be more intense.

Data science also has another hidden workload: keeping up.

You may need to stay current on:

1. Machine learning methods
2. AI tools
3. Cloud platforms
4. Data engineering basics
5. Experimentation
6. Model deployment
7. Prompt engineering and LLM workflows
8. Privacy and governance

Actuaries have continuing education too, but the skill change is usually slower and more structured.

If you want a predictable long-term profession, actuarial science may feel calmer.

If you enjoy constant change and new tools, data science may fit you better.

Job Security in 2026: AI Changes the Conversation#

Let’s talk about the elephant in the group chat: AI.

Data science is close to AI, so people assume it is automatically safe. That’s not totally true.

AI tools can now help with:

• Writing SQL
• Cleaning data
• Creating charts
• Building basic models
• Explaining code
• Drafting reports
• Generating Python scripts
• Creating dashboards faster

That means junior data science work is getting squeezed. Companies may hire fewer entry-level people and expect them to be stronger.

But strong data scientists are still valuable because companies need people who can:

1. Frame the right business question
2. Know when a model is wrong
3. Handle messy internal data
4. Explain tradeoffs to non-technical leaders
5. Connect analysis to revenue, risk, and product decisions
6. Manage privacy, bias, and compliance

Actuaries are also affected by AI, but differently.

Actuarial work includes judgment, regulation, capital requirements, audit trails, pricing responsibility, and professional standards. AI can speed up modeling and reporting, but it does not replace accountability easily.

Insurance companies are cautious. They do not want a mysterious model deciding reserves without explanation.

So actuarial job security remains strong, especially for credentialed professionals.

In 2026, the safest profile might be a hybrid:

• Actuary who can code in Python and SQL
• Data scientist who understands insurance, finance, risk, and regulation
• Analytics professional who can explain decisions to executives
• Risk modeler who understands both machine learning and actuarial principles

That hybrid profile can earn very well.

Education Requirements: Which Path Is Harder?#

Both paths are hard, just in different ways.

For actuarial careers, common degrees include:

• Actuarial science
• Mathematics
• Statistics
• Economics
• Finance
• Data science
• Computer science

You do not always need an actuarial science degree, but you usually need exams.

A strong entry-level actuarial candidate often has:

1. One to three exams passed
2. Internship experience
3. Excel skills
4. SQL or Python
5. Probability and statistics knowledge
6. Communication skills
7. Interest in insurance or risk

The exam path can take several years. Many actuaries take 5 to 8 years to become fully credentialed while working full time.

That is a serious commitment.

For data science, common degrees include:

• Computer science
• Statistics
• Mathematics
• Engineering
• Physics
• Economics
• Data science
• Operations research

A master’s degree or PhD can help, especially for research scientist, applied scientist, and machine learning roles.

But for many business data scientist roles, a strong portfolio, internships, SQL, Python, statistics, and product sense can be enough.

A strong entry-level data science candidate usually has:

1. Advanced SQL
2. Python
3. Statistics
4. Machine learning basics
5. Data visualization
6. Business case studies
7. Internship or project experience
8. Clear communication
9. GitHub or portfolio
10. Ability to explain tradeoffs

Which path is harder?

Actuarial science has harder formal exams.

Data science has a messier hiring market and faster skill churn.

Pick your pain.

Salary Growth: Exams vs Skills#

Actuarial salary growth is more predictable because exams create milestones.

A typical actuarial salary progression in the US might look like:

1. Entry-level with 1 to 2 exams: $70k to $90k
2. 3 to 5 exams: $85k to $110k
3. Associate credential: $110k to $150k
4. Fellow credential: $140k to $220k
5. Manager or senior specialist: $170k to $280k
6. Chief actuary, partner, or executive: $250k to $500k+

Of course, this varies by location and industry. Property and casualty, reinsurance, and consulting often have higher ceilings.

Data science progression is less standardized.

A possible US salary path:

1. Junior data scientist: $85k to $120k
2. Data scientist: $110k to $150k
3. Senior data scientist: $150k to $230k
4. Staff data scientist or applied scientist: $220k to $350k
5. Principal scientist, ML lead, or director: $300k to $500k+
6. VP Data, Head of AI, or chief data officer: $350k to $800k+

But not everyone reaches staff or principal level.

In data science, you need to keep proving impact. You can get paid amazingly well, but the path can feel less certain.

Best Cities for Actuary Salaries in 2026#

Actuary salaries are strong in insurance, consulting, and finance hubs.

Top US cities include:

• New York City: $90k to $250k+
• Chicago: $80k to $220k+
• Hartford: $75k to $210k+
• Boston: $85k to $230k+
• Philadelphia: $80k to $210k+
• Dallas: $80k to $210k+
• Atlanta: $75k to $200k+
• Minneapolis: $75k to $200k+
• Charlotte: $80k to $210k+
• San Francisco: $95k to $250k+

In Europe, strong actuarial markets include:

• London: £45k to £180k+
• Zurich: CHF 100k to CHF 220k+
• Munich: €60k to €160k+
• Frankfurt: €60k to €150k+
• Dublin: €55k to €140k+
• Amsterdam: €55k to €140k+
• Paris: €50k to €130k+
• Brussels: €50k to €125k+

London, Zurich, Munich, and Dublin are especially good if you want insurance, reinsurance, and consulting options.

Best Cities for Data Scientist Salaries in 2026#

Data science pay is highest where tech, finance, and AI funding are concentrated.

Top US cities include:

• San Francisco Bay Area: $130k to $300k+
• New York City: $120k to $280k+
• Seattle: $120k to $270k+
• Boston: $110k to $250k+
• Austin: $105k to $230k+
• Los Angeles: $105k to $230k+
• Washington, DC: $105k to $220k+
• Chicago: $100k to $220k+
• Denver: $95k to $210k+
• Raleigh-Durham: $95k to $200k+

In Europe, strong data science markets include:

• London: £55k to £180k+
• Zurich: CHF 110k to CHF 230k+
• Berlin: €60k to €140k+
• Munich: €65k to €150k+
• Amsterdam: €60k to €150k+
• Dublin: €60k to €150k+
• Paris: €55k to €140k+
• Stockholm: SEK 600k to SEK 1.3m
• Copenhagen: DKK 600k to DKK 1.2m

If you’re targeting Big Tech or AI companies, location still matters, even with remote work. Hybrid roles near hubs usually pay more than fully remote roles based in lower-cost areas.

Actuary vs Data Scientist: Which Has Better Remote Work?#

Data science has more remote options overall.

Many data science tasks are already digital, async, and tool-based. Remote data teams are common in SaaS, fintech, e-commerce, and startups.

But competition for remote data science jobs is intense. You may be competing with candidates across the US, Europe, India, Latin America, and Eastern Europe.

Actuarial remote work became much more common after 2020, especially in insurance and consulting. Many insurers now offer hybrid arrangements, and some actuarial roles are fully remote.

Actuarial remote competition may be slightly less chaotic because credentials and regulatory knowledge filter the applicant pool.

So the answer is:

• More remote jobs: data science
• Less crowded remote niche: actuarial, especially if credentialed

Which Career Is Better for Career Switchers?#

If you’re switching careers, data science may be easier to enter without starting from scratch.

You can build a portfolio, learn SQL and Python, take online courses, and apply for analyst roles first. Many people enter through data analyst, business analyst, BI analyst, or analytics engineer positions.

A realistic switcher path into data science:

1. Learn SQL deeply
2. Learn Python for analysis
3. Build 3 strong projects
4. Learn statistics and experimentation
5. Apply for data analyst roles
6. Move into data scientist work internally
7. Add machine learning after you understand business data

Actuarial switching is possible, but exams are the gatekeeper.

A realistic switcher path into actuarial work:

1. Pass Exam P or FM in the US, or an equivalent exam in your system
2. Learn Excel, SQL, and basic Python
3. Apply for actuarial analyst roles
4. Target insurance companies and consultancies
5. Keep passing exams while working
6. Build insurance domain knowledge

If you already work in insurance, finance, risk, underwriting, claims, or pensions, the actuarial path can make sense.

If you come from marketing, operations, tech support, economics, engineering, or general business, data science or analytics may be a smoother transition.

Personality Fit: Be Honest With Yourself#

Salary matters, yes. But you also need to pick the job you can tolerate on a random Tuesday in February.

Actuarial science may fit you if you like:

• Structured career paths
• Exams and credentials
• Insurance, finance, and risk
• Deep accuracy
• Long-term stability
• Regulation and professional standards
• Clear salary progression
• Quantitative work with business judgment

Data science may fit you if you like:

• Coding
• Messy data
• Fast-changing tools
• Product questions
• Machine learning
• Experimentation
• Building dashboards and models
• Tech culture
• Less formal credentialing

Avoid actuarial work if you hate exams. Seriously. The exams are not a small detail.

Avoid data science if you hate ambiguity. You’ll often be asked vague things like, “Can you find insights in the data?” and yes, everyone will expect magic by Friday.

Which Career Pays More in 2026?#

Data science pays more at the beginning and can pay more at the top, especially in Big Tech, AI, fintech, and high-growth startups.

Actuarial science has more predictable salary growth and excellent senior compensation, especially for credentialed actuaries in insurance, reinsurance, and consulting.

So the honest answer is:

1. Best starting salary: Data scientist
2. Best structured raises: Actuary
3. Highest possible ceiling in tech: Data scientist
4. Highest stability-adjusted pay: Actuary
5. Best for people who like exams: Actuary
6. Best for people who like coding and products: Data scientist
7. Best hybrid opportunity: Actuary with data science skills, or data scientist with insurance risk expertise

If you’re purely chasing money, data science has more upside early.

If you’re chasing high income plus a professional moat, actuarial science is underrated.

How to Choose Between Actuary and Data Scientist#

Use this quick checklist.

Choose actuary if:

• You can commit to years of exams
• You want a stable, respected profession
• You like probability, finance, and risk
• You want your credentials to carry long-term value
• You are interested in insurance, pensions, or reinsurance
• You want a clearer promotion ladder

Choose data scientist if:

• You enjoy coding and building with data
• You like tech and product problems
• You want higher entry-level pay
• You are comfortable with fast-changing tools
• You want more industry flexibility
• You prefer portfolios and projects over professional exams

Still stuck? Try this.

Look at 20 real job postings for each role in your city or target remote market. Save the ones that genuinely sound interesting. If you feel bored reading actuarial postings but excited by data science roles, that tells you something.

And if the opposite happens, listen to that too.

Final Verdict: Actuary vs Data Scientist Salary 2026#

In 2026, data scientists usually win on starting salary and top-end tech compensation. Actuaries win on structured career progression, long-term professional barriers, and stable high earnings once credentialed.

A junior data scientist at a strong tech company may outearn a junior actuary by $15k to $30k.

A fully credentialed actuary with 10 years of experience can absolutely outearn many data scientists, especially outside Big Tech.

The best choice is not just “which pays more.” It is which path you can stay motivated in long enough to become genuinely valuable.

Because that’s where the money is.

If you’re applying to actuarial analyst, data scientist, data analyst, or risk modeling roles, don’t let your resume get filtered out before a human sees it. Run it through JobRise’s free ATS checker here: https://jobrise.io/en/free-ats-checker/