Data Scientist vs Actuary: An Analysis of Opportunity Cost

By Ben Burnett

Anyone studying in a quantitative field must learn about data and actuarial science—the study of the financial implications of uncertain future events—before they graduate. If you are considering a career in data science, it’s important to learn about actuarial science; if you are in an actuarial program, you likely already know about data science. It is what these careers have in common but also what differentiates them that, if taken advantage of, could spell out a profitable career path for anyone.

Before data science became popular due to leaps in technology, actuaries were the data scientists and the wizards of the data analytics world. It has been observed by the Society of Actuaries (SOA), one of two organizations that handle the credentialing of actuaries, that “Actuaries have been losing ground to data scientists. Many insurers’ data science teams are growing rapidly and taking on tasks that sometimes overlap with actuarial work.”¹ 

Despite the tough competition, actuaries are extremely well-planted in their respective fields and their credentials are tough to beat. Actuaries are not going anywhere, and the demand for actuaries is even growing under current circumstances.^2;3

So which career is the better choice? Employers want data scientists these days, but actuarial science is an incredibly stable career with several advantages over data science. That begs the next question: which career has the lowest opportunity cost? To answer these questions, we will need to properly define each role and what the respective career path looks like. At that point, we can properly compare the opportunity costs associated with each profession. From there, we will explore a possible career path that encompasses both specialties while having the lowest opportunity cost of all.

Why Data Science?

“Over the past decade, the availability of data and demand for data science skills and data-driven decision-making has skyrocketed. Pushed further into the spotlight by the drastic shift in business operations and consumer behavior caused by the COVID-19 pandemic, analytics and data science are now cemented as essential navigational tools across industries and functions.”⁴

Big data is a modern commodity that companies cannot get enough of. In the twenty-first century, marketing, finance, and insurance companies are among an ever-growing list of businesses that compete using big data. “Insurers everywhere are moving into big data and analytics as fast as they can.”⁵ The ability to interpret data is a skillset that everyone in this generation needs.⁶

What Does a Data Scientist Do? 

What separates data scientists from actuaries is their ability to handle the challenges of big data. From organizing gigabytes of data streaming constantly to dealing with all sorts of unstructured data (e.g., audio, videos, large amounts of text, etc.), data scientists have the necessary skills to take information, analyze it, and make predictions with it.

“Data scientists . . . leverage data to create new product features and tend to do more modeling and open-ended research . . . When you watch Netflix and see a personalized list of recommended shows, that’s machine learning algorithms and data science at work.”⁷

A proficiency in predictive modeling, algorithms, and deep learning is what sets data scientists apart from other data analysts or data managers. These skills also make data science very comparable to actuarial science.

One of the more distinctive elements of data science, in comparison to actuarial science, is the very strong computer science background required for data science. Data scientists work using a variety of computational languages and platforms, such as SQL, SAS, Python, Hadoop, and Tableau.

What is a Data Scientist’s Salary?

According to a Burtch Works study from 2020, data scientists are the highest earners compared to other predictive analytics professionals. This is likely because data scientists tend to have postgraduate degrees. Another contributing factor to their high salary is the fact that their work requires a highly specialized skillset, but the talent pool of data scientists is relatively small compared to other professions.⁸

Table 1 shows that within the first three years of work, data scientists make a competitive salary, and if they stick with it for nine or more years, they can potentially double their initial salary. For those in a management position, the salary is even higher. Most data scientists work forty to sixty hours a week and 15% work from home.⁹

How Do I Become a Data Scientist?

Data science is still a relatively new career, and as such, it does not have a clearly defined career path. There is no governing body that handles credentials or certifications. Requirements for getting a job in data science are generally ambiguous and vary depending on the field of work. Technically, a postgraduate degree isn’t even required in a quantitative field such as computer science.¹⁰

Not discounting the importance of a postgraduate degree, employers are typically more concerned with a candidate’s experience as well as their soft and hard skills.¹¹ Data scientists who demonstrate strong experience, hard skills, and great communication through a portfolio have the best chance at landing jobs and earning promotions. Thankfully, due to such a low supply of data scientists, 61% of data science positions are available to those with a bachelor’s while 39% still require a master’s or Ph.D.¹²

Pursuing a career in data science can pose a challenge as it is still a new profession and expectations vary drastically, most notably in technical fields. It takes time and experience to build a robust portfolio and demonstrate hard skills. Typically, most data scientists need 1–2 years of in-field experience in addition to their degree.

Why Actuarial Science?

“Actuaries have been called the original data scientists.”¹³ Actuaries are a mainstay for insurance and reinsurance companies and have been for decades. As stated by the Executive Officer of the Society of Actuaries, “Actuaries are extremely versatile employees and are typically worthy investments for companies. We pride ourselves in being well-versed in statistical techniques, data handling, analytics, communications, and strategy development . . . actuaries are highly valued in their positions.”¹⁴

Insurance companies need actuaries, but actuaries are transitioning their skills into other sectors as well. Investment actuaries and enterprise risk actuaries are becoming increasingly common. Actuaries are, generally speaking, risk analysts and managers. Companies across many fields—especially those in insurance or finance—have risk in some way and need actuaries to help mitigate it.

What Does an Actuary Do?

“Actuaries analyze the financial costs of risk and uncertainty . . . and they help businesses and clients develop policies that minimize the cost of that risk.”¹⁵ For example, an actuary will determine the probability of various events or financial risks to calculate the amount of money a company needs to have in reserves, or in insurance, to cover such losses. “In essence, actuaries predict the financial future of a company with math and science.”¹⁶

Performing these analyses requires a great deal of data and the right tools. Actuaries are expected to know computational languages typical to the industry, including SQL, SAS, R, and Excel VBA. Though an actuary’s computer science expertise is not as extensive as a data scientist, it is similar. Table 2 depicts the nuances of actuarial science compared to data science.

How Much Do Actuaries Make?

Like most professions, years of experience have the largest effect on an actuary’s salary. What makes actuaries unique is the examination process that takes place during employment in order for actuaries to earn their credentials. Companies offer salary increases for each exam an actuary passes and more significant increases for obtaining “associate” or “fellowship” status, as seen in Table 3. This expands the control an actuary has over their salary and gives them a higher earning potential early on in their career.

 

How Do I Become an Actuary?

Actuaries have been around for decades and benefit greatly from large, highly regarded actuarial associations. This is an incredible boon for actuaries because they do not have to rely solely on a degree to accredit them. There is far more structure around the actuarial career path.

Entering the actuarial field requires only a bachelor’s degree in a quantitative field, like math or statistics. No work experience is necessary to enter the field; however, one or two exams are needed to get an actuarial job. Where many other students must get a master’s to obtain the same earning potential, it is not required for an actuary. Having these actuarial societies decreases the transition time from graduation to employment, allowing actuaries to earn a higher salary in less time.

Most actuaries are able to reach “associate” status within 4–7 years. After 2–3 additional years, they can obtain “fellowship” status. For several financial industries, especially insurance, these credentials are the equivalent to earning a master’s degree and a Ph.D. respectively.

The Opportunity Costs for Both

If a data scientist decides to pursue actuarial science later in their career, it could potentially set their career and salary back four to seven years.

“The actuarial profession is better defined and has more defined standards [than data science]. An actuary can work in a data scientist role, but a data scientist typically cannot work as an actuary.”¹⁹

Another key opportunity cost of choosing data science is the risk involved with an ambiguous career path. Going into data science requires one to be in charge of their career path and make their own strategy for employment independent of any larger associations or societies. There is also the inherent risk of missing out on job opportunities based on choices regarding school and building a portfolio that do not meet employer expectations.

“Data scientists bring new skills [to actuarial work], particularly programming. Sometimes data scientists are perceived as operating with fewer constraints compared with actuaries. Many young people may also see data science as a more versatile career option that offers opportunities to work across industries.”²⁰

There are many job options for an actuary: life and annuities, healthcare, pension, property, and casualty, automotive, reinsurance, investment, enterprise risk, and consulting. Each sector has its distinctions and offers a different experience. Still, at the end of the day, actuarial science cannot compete with the range and versatility of data science. Or could it?

The Actuarial Field is Adapting

The actuarial field is adapting to the leaps in technology, and some might even argue, that it is staying ahead of the latest advancements. Teams that manage big data are filled with data scientists and actuaries. “Actuaries on these teams may be thought of as the subject matter experts. But actuaries may be positioned to be the quarterbacks of the Big Data teams. With the proper background, an actuary can understand and direct the work of the Big Data… team.”²¹

While we have established that data scientists cannot readily start working as actuaries, without setting their career back four to seven years, the same is not true of actuaries looking to transition into data science. While experience in the actuarial field may pigeonhole some individuals to work only in insurance, the unstructured nature of a data scientist’s career path coupled with the low supply of these highly demanded workers suggests a possible opening for actuaries who want to transition into data science.

Recently the SOA changed their curriculum for the “associate” track. The new curriculum now includes a greater emphasis on predictive analytics and data science. In addition, the SOA is offering micro-credentials, one of which is in data science. The SOA is also planning to conduct outreach programs to promote these credentials with employers and build awareness.²²

A Possible New Career Path

These changes to curriculum paired with the ambiguity of a data scientist’s career path and the lowered standards due to high demand create an incredible opportunity. Suppose you want to go into data science, but you do not like the instability of the career path. An aspiring data scientist could earn a bachelor’s and pass two actuarial exams to enter the professional space as an actuary.

Now in the professional space, a data scientist could begin to build their portfolio by taking every opportunity to get involved with projects and teams working on predictive analytics and data science. They could do all this while working on receiving their credentials as an actuary—specifically their micro-credentials for data science.

“There is not yet a Society of Data Scientists that requires rigorous professional standards. Data scientists can have anything from a bachelor’s degree to a Ph.D. The distinction between an actuary and a data scientist may gradually disappear.”²³

This career path allows the opportunity to continue with actuarial science or transition into data science but with a professional portfolio and credentialing from the SOA. There may still be some employers who still want a postgraduate degree, but many others who may prefer the credentials from the SOA.

The Lowest Opportunity Cost

“The evolving nature of the relatively new field [of data science] means career paths are flexible. … By expanding knowledge in artificial intelligence, statistics, data management, and big data analytics, a data analyst [like an actuary] can transition into a data scientist role.”²⁴

Bottom line is, regardless of whether you want to be an actuary or data scientist, it will be most beneficial to you to make the decision that involves the lowest opportunity cost.  This looks like taking data science classes, becoming involved in machine learning, and mastering as many coding languages as you can. At the same time, take the first two actuarial exams. Whether you go into actuarial science or not, putting those exams on your resume means a lot to many professionals and will certainly make you stand out to future employers. Keep your opportunities open and consider a career in actuarial science as it allows you the option to transition into data science or stay in the actuarial field.

Conclusion

COVID-19 has been the crucible for many careers in the last couple of years. “The fact that 45% of organizations are keeping analytics front and center could be part of the reason why layoffs and furloughs are still the exceptions for analytics teams. Being critical to navigating the crisis may be helping to insulate analytics teams.”²⁵ This further illustrates how important data scientists and actuaries are. They offer great job security, high salaries, and countless opportunities to expand one’s expertise. Whether you decide the opportunity cost of data science or actuarial science is worth it or not, it is important to understand the roles they play in a growing number of companies.  There is a good chance that no matter what field you choose you will be working with a data scientist or actuary someday.

Notes

1. Chester, Ari and Erwann Michel-Kerjan. “Reimagining Actuaries: A Q&A with Society of Actuaries’ Greg Heidrich.” McKinsey Insights, August 26. http://erl.lib.byu.edu/login/?url=https:// www.proquest.com/magazines/reimaginingactuaries-q-amp-with-society-greg/docview/ 2437259193/se-2?accountid=4488.
2. Chester and Michel-Kerjan, “Reimagining Actuaries”.
3. “Actuaries : Occupational Outlook Handbook.” U.S. Bureau of Labor Statistics.U.S. Bureau of Labor Statistics, September 8, 2021. https://www.bls.gov/ooh/math/actuaries.htm#tab-1.
4. “Building a Career in Data Science and Analytics: The Ultimate Guide.” edX Blog. edX, April 1, 2021. https://blog.edx.org/data-science-analytics-career-guide.
5. Chester and Michel-Kerjan, “Reimagining Actuaries”.
6. “Building a Career in Data Science”.
7. “Building a Career in Data Science”.
8. Burtch, Linda. “The Burtch Works Study Salaries of Data Scientists & Predictive Analytics Professionals.” BurtchWorks.com, August 2020, 1–55. https:// www.burtchworks.com/wp-content/uploads/2020/08/Burtch-Works-Study_DSPAP-2020.pdf.
9. Burtch, “The Burtch Works Study,” 10.
10. “Building a Career in Data Science”.
11. Zita, Christopher. “Is Data Science Still a Rising Career in 2021.” Medium. Towards Data Science, November 8, 2021. https:// towardsdatascience.com/is-data-sciencestill-a-rising-career-in-2021-722281f7074c.
12. “Is Data Science”.
13. Chester and Michel-Kerjan, “Reimagining Actuaries”.
14. Siddique, Rehan. “Is It Still Worth Becoming an ACAS/FCAS?” CASAct.org, September 1, 2020. https://www.casact.org/ newsletter/article/it-still-worth-becomingacasfcas.
15. “Actuaries : Occupational Outlook Handbook.” U.S. Bureau of Labor Statistics.U.S. Bureau of Labor Statistics, September 8, 2021. https://www.bls.gov/ooh/math/actuaries.htm#tab-1.
16. Probasco, Jim. 2021. “What to Know about Actuaries – the Professionals Who Predict the Financial Future of Companies with Math and Science.” Business Insider, Sep 03. http://erl.lib.byu.edu/login/? url=https://www.proquest.com/newspapers/what-know-about-actuaries-professionalswho/docview/2568764184/se-2? accountid=4488.
17. Sondergeld, Eric, and Norah Denley. “Analytics and the Actuary.” SOA.org. Society of Actuaries, 2017. https:// soa.org/globalassets/assets/files/staticpages/sections/analytics-and-the-actuaryreport.pdf.
18. “Actuarial Salary Surveys for Actuaries.” Ezra Penland Actuarial Recruitment. Ezra Penland Actuarial Recruitment, October 14, 2021. https://www.ezrapenland.com/salary/.
19. Sondergeld and Denley, ““Analytics and the Actuary,” 7.
20. Chester and Michel-Kerjan, “Reimagining Actuaries”.
21. Chester and Michel-Kerjan, “Reimagining Actuaries”.
22. “ASA Curriculum Changes & MicroCredentials FAQs.” SOA.org. Society of Actuaries, November 2021. https:// soa.org/education/general-info/asamicro-credentials/.
23. Chester and Michel-Kerjan, “Reimagining Actuaries”.
24. “Building a Career in Data Science”.
25. Burtch, “The Burtch Works Study,” 7.