Actuary vs Data Science Professional: What’s the Difference?


Data science and actuary careers have many similarities, yet they’re two very distinct fields. It’s been said many times that while a data scientist can do actuary work, an actuary is not trained to work in data science. Learn the attributes of both here.

What is Data Science?

Data science is an interdisciplinary field that combines:

  • advanced analysis
  • statistics
  • machine learning
  • computer programming

Data science deals with small data, big data and all kinds of data. Unlike actuaries, who deal most with numbers and statistics and the insurance industry, data scientists work with all kinds of data, including:

  • numbers
  • text
  • pictures
  • surveys

Most data scientists have master’s degrees while some advance their education and pursue doctoral degrees.

History of the Professions

In discussing a data scientist vs actuary, there is reason to look at the history of both. Actuaries existed in the mid-1600s. They resulted from an early form of insurance company that formed to protect the interest of rich investors who funded cargoes in a time of uncertain shipping. Professional “risk-takers” evolved who would insure the financial risks of the speculators. Even earlier was another form of insurance, the pension. It was a surety against outliving one’s assets. Both the shipping and the life insurance required assessing the risks, one of the voyage and the other of the person’s chances of succumbing to accident or illness. The profession of actuary was born.

Having existed as a profession only about ten years, data science is the “new kid on the block.” Of course, society has collected data far longer than that.  Industries such as healthcare and insurance have relied on professionals who pored over page after page of statistics to recognize trends and predict financial occurrences. Since computer use has become so common, more and different kinds of data have been collected.  The age of big data has dawned. There now is a huge repository of general facts and figures. With the emphasis on artificial intelligence and machine learning, the need has grown for professionals who can use these advances to mine the massive field and extract specific facts and numbers.

In fact, data science is such a new field that business giants such as Bloomberg are funding grants for people who will research areas like “natural language processing” and machine learning. Experts estimate that the world’s data doubles every year-and-a-half. Every 60 seconds, according to webopedia, there are more than 204 million emails sent, more than 2 million Google searches and, more relevant to this article, more than $270,000 spent on ecommerce. Many data scientists began their careers in statistics, but as the amount of data surpassed the available experts equipped to mine it, their expertise evolved to data science.

What Data Scientists Really Do

So, in general, data scientists help decipher the huge influx of data, but what do they really do? They take huge amounts of unstructured data and categorize it, simplify it and edit it to make it understandable to corporate boards, universities, government organizations and others. They organize numbers and statistics, but they also categorize and translate language. Data scientists are deeply cognizant of innovations in machine learning and artificial intelligence and keep abreast of new ways to use them. They isolate trends in business and bridge the gap between corporate and IT communication. Additionally, Forbes Magazine rates data science as the best job in America, as it scores highly for both job score (compensation, etc.) and job satisfaction.

There are two types of data scientist, according to an article on the website The first type is the data engineer, who mines the information from the data, cleans it of irrelevant information, translates it into “standardized format” and puts it into a data repository. The second type takes the data from the repository and uses it to design models using machine learning algorithms.

Many of these tasks are being assumed by artificial intelligence automation as it is perfected. The article says that the best results are realized with a combination of both the human scientist and the artificial intelligence. So, in the utilization of machine learning, the data scientists both design the mechanics, and perform the lower-level tasks that enable them.

What is an Actuary?


An actuary is a trained professional who analyzes financial data to determine risks, probabilities, and uncertainty. Through the use of statistics, mathematics and financial theories, actuaries are able to assess and predict the risk of potential circumstances or events. Actuaries may work in other industries that rely on risk management but are most commonly found working for insurance companies. They create tables, reports, and charts to explain proposals and calculations.

Actuaries design and test insurance policies using various types of data to determine risk factors so they know what premiums should be charged for minimum risk and maximum profitability. Although an individual can become an actuary with a bachelor’s degree, the U.S. Bureau of Labor Statistics (BLS) states that it can take from four to seven years to earn the associate-level certification and another two to three years to earn the status of fellowship.

Data Science vs Actuarial Science Professionals: How are they Different?

A data scientist can work in a variety of settings including:

  • academia
  • industry
  • healthcare
  • government
  • human services

They can work anywhere data is collected. As stated, that means they work everywhere. Actuaries are more limited in scope. Yes, they work in the insurance industry, but they are found anywhere it is important to understand risk. For instance, the website says they are helping the government determine the effects Covid-19 will have on Medicaid and managed care. The organization says that actuaries are also studying the effect of Covid layoffs and furloughs upon pension plans, too. Additionally, with an eye to the California wildfires, this organization of actuaries released a paper that examines where and when wildfires occur and determines how the cost of property loss and casualties will rise. It has been found that almost 72 percent of actuaries work in the insurance industry, while about 17 percent worked as consultants. The remainder were in corporate management.

These professionals work in virtually all industries. For example, recent online listings of job openings for data scientists included:
• An opening in the National Security Agency
• A data scientist job for Lockheed Martin Space
• A senior manager in advertising analytics
• A job in the veteran’s administration
• And others

Actuarial Science vs Data Scientist: Difference in Education


While the educational requirements to become actuaries begin similarly, they diverge during the undergraduate program. Actuaries can hold bachelor’s degrees in almost any discipline. They can even major in liberal arts. They must, however, have courses in mathematics and computer programming. Before earning certification, students should take courses that pertain specifically to the actuarial profession because the certification exams are difficult. There are seven exams on the path to associate certification and three more to become fully certified at the professional level. Most actuaries work and continue their educations, taking the tests as they can until they become completely certified. There are several different certifications actuaries can earn including:

  • Associate of the Society of Actuaries
  • Fellow of the Society of Actuaries
  • Chartered Enterprise Risk Analyst

When comparing the educational requirements for a data science vs actuary professional, a data scientist should have a degree in:

  • statistics
  • computer science
  • information technology
  • data science

They must understand and have a mastery of computer programming languages. After getting an undergraduate degree, computer scientists generally earn certifications in specific skills and computer languages. Data science is a field where promotion depends upon education, so most data scientists go on to graduate studies.

Certification of data scientists can be done according to their specialties. Many certifications are offered in programming languages, and there are some that offer the equivalent of data scientist “training wheels.” IBM, for example offers a three-month certification program that includes nine fundamental courses such as;

  • What is Data Science
  • Data Science Methodology
  • Python for Data Science and AI

There is also a certification for business analytics specialists. At the advanced level, professionals can be certified by the SAS Academy of Data Science which offers several programs that can last from a “few months to several years.”


Since data science jobs have become so desirable, many actuaries are “rebranding” themselves as data scientists. This requires some major career renovations. For one thing, it is difficult to move from the structured field of actuarial data to unstructured big data that may be harvested even from social media posts. Being comfortable with the use of artificial intelligence is a quantum leap as well, but necessary for handling the huge increase in data.

Data science may, however, reduce the number of actuarial jobs currently available. The website notes that the American association Casualty Actuarial Society has created a new certification in data science. Additionally, since both deal in future modeling and the primary difference in the professions is that data scientists have a proficiency in computer technologies, it seems likely that data scientists could assume the duties of an actuary by looking at all the information in terms of data sets.

Actuaries, however, use data to predict future financial trends. Data scientists analyze data, a lot of data, to identify patterns that may be used for forecasting. Although human instinct and input cannot be replicated by machines today, data scientists are designing computers and programs which may be used to perform many of the actuarial duties. There are a lot of advantages to this.

• Computers are faster
• Computers are more accurate
• Automated systems may be cheaper in the long run

So, if an automated system is perfected, data scientists may replace actuaries. Another possibility is that the delineation between the professions will blur as more programs are automated in the interest of handling increased data and professionals are required to have extensive computer skills and knowledge. The best way for actuaries to protect their jobs is to gain expertise in computing and the other skills needed in data science.

Can Data Science Replace Actuaries?

Data scientists and actuaries are as different as they are alike. They have similar skill sets, educational requirement, and responsibilities. They both analyze data to make educated future predictions and use a lot of the same techniques while doing so. Both actuaries and data scientists crave data because the more data they get, the better they can be at their prospective jobs.  Because data science is such a broad field with so many areas of specialization, whereas actuaries are generally geared toward insurance-related jobs, a big question today is if data science will replace actuaries. The answer may vary depending on which professional is asked. 

Their responsibilities might be similar, but their actual duties are different. Actuaries are found mostly working with insurance companies. Their duties include estimating costs of losses, predicting the likelihood of loss and advising as to what should be charged to cover the loss and be profitable. Data scientists, on the other hand, can be found in almost any industry and will be given much harder problems to solve. In addition to solving the problem or answering the question, they also have to design the question. Based on these facts and the importance of both. there is little chance that data science will replace actuaries

Career Outlook for Both


When assessing actuary vs data scientist salary, the career outlook is very good for both types of roles. A job growth of 21 percent is predicted for actuaries during the 2021-2031 decade, according to the BLS. Salaries for data scientists ranged from $69,000 to more than $136,000 while actuary wages ranged from $50,240 to $145,921, according to a PayScale August 2022 report.

Although we tend to think of data science as being a new field, it’s actually been around since actuarial science was first used in the 1980s. While the career paths, duties, and responsibilities of actuaries and data scientists tend to intertwine, it’s highly unlikely that data science will replace actuaries because they both have their importance.

It is certain, however, that as machines learn to imitate human instinct and “hunches,” data science will gain an edge over actuaries which may lead to merging the professions.  Actuarial data science is the intersection of the two fields.  This may not happen for decades, but data is increasing exponentially, and machine learning is adapting to human tasks to meet the needs of this exploding field. Additionally, the ability of actuaries to keep up with an ever-changing database is being taxed. so, the question posed at the beginning of this article, “actuary vs data scientist: what is the difference,” may be best answered with the qualification, “assuming there is a difference.”

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