I think it’s an unfair comparison. Of course the average actuary wouldn’t even come close to the average days scientist when it comes to data science skills.

Just like the average doctor wouldn’t, the average lawyer wouldn’t, and the average banker wouldn’t.

The problems they solve require very different skills. The problems I solve on a day to day basis have no need for data science. There are certain areas in my company where we use data science heavily, but in the areas we do need to use it we just use actual data scientists.

The SOA, in my opinion, is going down the wrong path. Actuaries shouldn’t be trying to compete with data scientists. It has such little relevance to the work of most actuaries and in my opinion it’s just a way for the SOA to try and increase their revenues by jumping on a bandwagon.

I agree with you “hit and miss” observation though.

The exams are a barrier, but they’re not perfect. Plenty of mediocre people slip through the net. My company has roughly 100 actuaries. There are maybe 10-15 people that I’d consider very good at their job. The rest are just “meh”. I suspect this is the same in most fields though. Most companies now seem to have some sort of “data science” function. Unless the company is a tech or “data is the product” company, those data science functions don’t seem to contain the brightest data scientists. A friend of mine is a data science lead at retail company, and he’d be the first to admit he’s fairly mediocre. Most of their challenging work is outsourced to a specialist consultancy and their in house staff barely do any actual “science”.

The question was about whether actuarial credentials should be positive signals in an application to a data science job. And my answer is: all else being equal, having more credentials is arguably good. However, all else is not equal. Having invested in actuarial credentials is a fairly strong signal of very limited computational/programming skills which are critical to a data science career. So the net signaling value of actuarial credentials in data science is actually negative.

I'm well aware that data science is as awash with mediocrity as any other field, but not being able to function outside of Excel makes you mostly useless as a data scientist, not merely mediocre.

I also agree with you that actuaries should not compete with data scientists, and should mostly stick to product, actuarial finance, reserving, etc. unless they desperately want to do data science, in which case they should switch to data science.

> in my opinion it’s just a way for the SOA to try and increase their revenues by jumping on a bandwagon.

It's worth noting that this is exactly what you'd predict if you buy my original thesis about the exams.

I agree completely with your last paragraph. If I wanted to be a data scientist I’d be a data scientist. I don’t want my actuarial organisation wasting time and money trying to market us as “data scientists” when we are not data scientists. It’s a completely different job. It’s almost comparable to the profession deciding that we should now market ourselves as lawyers because we also contribute to writing contracts and treaties.

I would argue though that being an actuary doesn’t automatically make you bad at anything outside of Excel.

I’m not a data scientist, but I’m sure if I decided to go down that route my skill set would put me in a good position.

I have a very good grounding in statistics and probability.

I use R/Python quite heavily, regularly building models from scratch. I work with GLMs, Copulas, Monte Carlo simulation etc.

I deal with big volumes of data and have to write efficient algorithms to deal with it.

Most of my skills didn’t come from the exam path, but doing the exams also didn’t make me bad at all those other things.

Just being an actuary obviously doesn’t mean you have those skills though, which is what I was getting at with my “hit and miss” comment.

> I would argue though that being an actuary doesn’t automatically make you bad at anything outside of Excel.

I agree, I just think it's statistically a net negative signal if conditioned only on years of experience. I would expect a generic technical BS or MS in a DS-relevant field to be more qualified than someone who passed actuarial exams, if the two are at a similar point in their careers.

That said, competent employers shouldn't be relying on unconditional signals, they should be interviewing and testing and getting more information on the candidate. For such employers, the signaling value of the actuarial credential ought to be neutral.

Keep in mind that the actuaries who apply for data science jobs are a heavily self-selecting subset. Among the candidates who apply for data science positions, I think actuarial credentials are a strongly positive signal on business acumen (especially but not only in insurance) and a weakly positive signal on quantitative skills relevant to the generic technical BS or MS degree holder at the same level of experience.

I agree that the median actuary would do poorly in data science, mostly because they don't have the programming ability for it. But the median actuary isn't trying to be a data scientist, so that's not who you care about if you're hiring for a data science position.

Interesting but hasn't been my experience. I find that actuaries going into DS are what you'd naively expect. They know the stuff that's tested on the exams but have underdeveloped DS skills compared to other quantitative types at the same career stage, especially in coding. I would definitely not describe them as strong in business acumen.

Anecdotally, most of the people I’ve known who’ve switched out of actuarial to data science haven’t been the best.

In most cases it’s been people who couldn’t pass exams, or were missold on what actuarial work actually involves and ended up moving early in their career when they wouldn’t have strong business acumen at all.

The one person who made the switch who I would consider a good actuary went back and did a masters in data science and applied for entry level roles. He had a couple million in the bank and decided he could afford to start over again.

He’s doing well now, he’s in a relatively senior position at a tech company. Financially he’s worse off than he would have been if he continued down the actuarial path, but he enjoys data science work a lot more than his old job and isn’t limited to insurance/pensions/finance.

this is true — as an analyst, I was severely reprimanded for writing data cleanup scripts since they couldn't be reviewed in vanilla Excel

Incredibly depressing. I hope you got out.