Let’s End the Debate – Actuary vs Data Scientist
Which career is better and why I am choosing to do both
Despite being regarded as the first generation data scientist, most people probably don’t know what an actuary is or what exactly they do. Heck, I wouldn’t have known this job even existed if it wasn’t for my dad who told me about it.
Yes, it is very much a niche field that traditionally relates to working at an insurance company or pension fund, but things have now changed. Because of their rigorous background in statistics and ability to analyze data, many actuaries have slowly transitioned to become data scientists and found great success in doing so due to the substantial overlap in skillset between the two fields.
As someone who studied an actuarial major at university and now working as a data scientist, I felt compelled to share my own experience.
This blog post aims to explore the two professions, actuaries versus data scientists, specifically:
- What they do day-to-day
- Where they work
- How to qualify or become one
- Salary and career progression
- Work-life balance
In the second part of this blog post, I will focus more on my own personal experience pursuing the actuarial qualification while working as a data scientist, why I decided to split my time between the two and perhaps why should you consider doing the same.
What does an actuary or data scientist do?
The big word that you will hear people use when explaining the role of an actuary is risk.
Ever wondered why different people pay different amounts for their car insurance premiums? Why does a married man in his late 30s, living in a quiet suburb with two kids pay a lower premium than a newly qualified driver in his early 20s, living in the city and driving a sports car? You guessed it, the answer is risk!
But what variables do we use to determine these premiums and what exactly should each individual with different risk profiles pay? That is where an actuary comes in.
Actuaries rely on statistics (particularly the law of large numbers) and financial mathematics to determine the risk level of a particular event and subsequently recommend the appropriate action to properly manage this risk.
Data scientists, on the other hand, spend the majority of their time manipulating and visualizing data and more importantly, getting actionable insights from data. Unlike actuaries, however, data scientists tend to work in a wider range of industries and not just in financial services.
As you can probably see, the nature of problems that actuaries and data scientists are hired to solve can be quite different.
Actuaries typically deal more with financial risk as well as helping organizations prevent solvency issues while keeping the business profitable. Effectively, this can include pricing of insurance products, capital reserving, liability valuations, and so on.
Data scientists, on the other hand, solve a wider variety of problems. A data scientist’s main goal is to find patterns within data and use these patterns to inform decisions like resource allocation and product personalization for consumers. Patterns can be in the form of many things such as customer transactions, weather, road traffic, just to name a few.
Where do they work?
The two main paths an actuary or data scientist can choose are either consulting or industry.
Consulting means to become a trusted advisor to clients from different industries whereas industry means to work specifically in the industry that your employer operates in.
In Australia, an actuarial consultant can work in a boutique actuarial consultancy like Finity or Taylor Fry or join the actuarial services team in one of the Big 4 accounting firms. A data scientist working in consulting, on the other hand, can work in any consultancies where there is demand for their skills. Some examples include Quantium (where I currently work), BCG GAMMA, and so on.
On the flip side, an actuary working in the industry usually find themselves in more traditional fields such as life insurance, general insurance, and superannuation. An industry data scientist, however, is much more flexible in where they can work. Some examples include but are not limited to supermarkets, banks, government, or even technology companies and start-ups like Canva.
How to become an actuary or data scientist?
Now that we understand the role of an actuary and a data scientist, let’s look at how to actually become one.
Exams, exams, and more exams – the road to becoming an actuary is notoriously lengthy and arduous. One typically takes around 5–7 years before becoming a fully qualified actuary.
So, if you are put off by the idea of sitting for exams well after university and studying while working full-time, you might want to reconsider if this is the right career option for you.
For most people, the journey usually starts with studying for an actuarial science degree at an accredited university in order to get as many subject exemptions as possible. This not only helps with laying the right foundation before starting your first actuarial job therefore also improving employability but more importantly, minimizes the number of outstanding exams that you will need to sit for once you start work.
For more information, I highly suggest looking into the professional governing body for the actuarial profession in the country that you live in. For instance, the Actuaries Institute in Australia, the Institute of Faculty of Actuaries in the UK, and the Society of Actuaries in the US.
The journey to becoming a data scientist, on the other hand, is somewhat less structured and clear-cut. It is also highly dependent on the country that you live in and the company that you want to work for.
Based on my own observation, if you are applying for a data science role in the US, you would typically need a Master’s or sometimes even a PhD, whereas, in Australia, you can probably get away with just a Bachelor’s degree as long as it is from a technical background such as engineering, mathematics, and statistics or computer science.
Salary and career progression
After speaking to a few of my peers, I have come to conclude that salaries that pretty on par between the two professions, at least at the Graduate level.
However, an actuary’s compensation is much more correlated with the number of exams a person has passed especially in the first couple years of your career. This goes to show just how important exams are if you want to become an actuary.
For a data scientist, however, salary is not dependent on the number of exams passed but instead on the industry that you work in as well as the level of experience.
Though there is one thing I can safely say for both professions, your salary is highest once you reach the manager or executive level where it becomes much less about the technical work that you do, but more about being able to lead a team and lead a project from start to finish.
So, if your goal is to climb the corporate ladder, be ready and take the initiative to exercise leadership and client management skills because those will be the skills that really matter once you start progressing to the top.
Work-life balance
I am hesitant to put a concrete number to this because I think it can change quite drastically depending on the industry and the time of the year.
Nevertheless, a consultant tends to work longer and less predictable hours compared to someone in the industry. This is because a consultant lives by the timeline of the project that he or she is working on and at times might even be juggling between multiple projects.
Working in the industry is more predictable. Barring reporting season or internal deadlines set by the company, hours in the industry tend to be more flexible.
The reason why work-life balance can be an essential factor to consider especially for aspiring actuaries is because you will most likely be required to do additional studies while working. Hence, if your work is already taking up a huge amount of your time and mental effort, this will not only reduce the time you have to prepare for exams but also time outside of work such as personal hobbies, relaxation, time with family and friends, and so on.
Why I decided to do both
Here comes the narcissist part of this blog post where I talk mostly about myself, specifically my decision to continue pursuing the actuarial qualification despite currently working as a data scientist.
Some of the reasons that I will outline here are more personal than the others but I hope by sharing them, will give you a deeper context of what we have discussed so far as well as help you decide between the two career paths.
Reason 1: Ego and sunk cost fallacy
Okay, I admit this is probably not the most rational decision but having already spent 3 years in university studying so deeply about the insurance industry, learning various complex financial mathematics formulas for valuing annuities and how to measure probabilities of life and death, there is a big part of me that is unwilling to simply give all that up for nothing.
There is also a part of me that sees this as an opportunity to push myself intellectually, to keep my skills fresh but more importantly, to experience the satisfaction of overcoming something that is difficult and not many people are willing to do.
Reason 2: Flexibility and future opportunities
Despite working as a data scientist now, I don’t completely discount the idea of one day switching back to becoming an actuary.
That is because I do personally quite enjoy the knowledge, concepts, and ways of thinking about a problem that an actuarial education provides. And although I became a data scientist because I wanted to work on a wider range of projects, I certainly wouldn’t mind going back to my actuarial roots and taking on problems that are more related to finance and insurance.
Furthermore, the actuarial qualification is also globally recognized and that can open the door to potentially working overseas if I wanted to.
Reason 3: Upskilling as a data scientist
Many of the things that you will learn initially before you become an actuary, especially in the first couple of exams are directly applicable to a data scientist.
Barring a few economic and accounting concepts or how to value financial derivatives, being able to understand probability and statistics, how to code in R or use Excel, or even machine learning is good knowledge for any data scientist to have.
Therefore, one of the reasons that I am persistent in continuing my actuarial education is to retain and further improve my knowledge in these areas because I strongly believe they will pay dividends, in some way or the other, in my day-to-day responsibilities as a data scientist.
I hope this blog post has managed to shed some light or even help some of you discover a whole new career path that perhaps you never knew before. More importantly, I hope by sharing what I know and highlighting the distinction between actuaries and data scientists, you are now more informed about the two professions and more equipped to decide which one you want to pursue.
Personally, I think both are remarkably rewarding careers with great prospects. It ultimately comes down to what you prefer and what you enjoy doing.
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