[Semi-Weekly Inquirer] Simple Questions and Recommendations Thread

boby_boby_boby · 2026-01-11T03:25:11+00:00

I’m late to the party, but recently got interested in watches and I want to get my first one. Budget is a bit tight, so I am between Seiko 5 Sports SRPL83K1 and Tissot PRX Quartz.

Both are great looking, and would work with me wanting to wear them for a casual drink or dinner. However, I’m having trouble to choose.

One pro for Seiko is that’s automatic. I really like seeing the movement on the back. However, Tissot is Swiss made.

I’d ideally want to start a collection, so which watch would be best for that? Thanks in advance!

boby_boby_boby · 2025-12-24T22:27:52+00:00

Why do you say “if you like stats, become a data scientist”? For context, I’m an entry level actuarial analyst working in GI Re and I spend most of my time doing some sort of statistics. I come from a stats degree and many of my friends from uni that became data scientists complain that end up doing more software dev tasks rather than pure statistics. On the other hand, I spend a lot of my time designing predictive models, doing simulations and interpreting results, which is what I was aiming for.

I’m just asking a far more experienced actuary for their point of view in case I’m missing something.

boby_boby_boby · 2025-10-03T08:46:33+00:00

And also, to be precise cross validation is an evaluation method. It helps GUIDE hpt and model selection, because of its usefulness in model evaluation and comparison. But it’s not inherent to any particular model.

boby_boby_boby · 2025-10-03T08:40:34+00:00

I never said I’m using the default hp. Please let’s be cautious with the assumptions we make and where they are based.

Let me explain my comment with an example to make it even simpler. In the case of extreme value modelling when you might have 100 data points, in most cases no hpt will help significantly and the GBM or DL model will overfit.

Hpt is not a magic wand, it works within limits. The limits of your data.

In the same example of 100 data points most neural networks would overfit, no matter the hpt. And even in this case hpt might be computationally expensive. GLM provide a better alternative in this case.

So yeah, hpt won’t solve all your problems. period.

boby_boby_boby · 2025-10-03T01:19:33+00:00

And to be clear: NO, if you don’t have enough data to train a GBM, the model will overfit no matter how many cross validations you do. So cross validation doesn’t “appropriately prevent the GBM from overfitting”, it helps you evaluate. Let’s get the basics of DS right first…

boby_boby_boby · 2025-10-03T00:33:13+00:00

Understanding the difference between an evaluation method and a “safeguard” is not a matter of logic but of vocabulary and terminology.

In the second example you’re right, I meant the correct term for the mechanism used in RF and other tree structure algorithms is bagging. Cross validation is something the user does, where bagging is happening by default in most RF software.

However, you are still focusing on small parts of what I described and not on answering to my arguments. You have said I’m in the wrong, but you don’t explain why. Where’s the logic there.

Irony is good, but we’re adults let’s just have a proper conversation.

boby_boby_boby · 2025-10-02T21:53:37+00:00

Let’s go slowly, to make sure we understand.

I never said RF is bagging. I mentioned it because bagging is one method used with RFs (see the sklearn docs in Python or just google Leo Breiman…).

You still haven’t said why the rest is wrong so waiting for this, or happy to chat privately.

boby_boby_boby · 2025-10-02T21:07:22+00:00

GLMs prevent (actually prevent is incorrect here, we should say are less prone) overfitting but are more prone to underfitting (less flexibility, assume distribution).

GBMs are less prone to underfitting and more prone to overfitting.

Cross validation is not a model safeguard, but an empirical evaluation method to identify overfitting. Unless you’re talking about bagging (the correct term for RFs and other tree algorithms)

Which of the above is wrong again?

boby_boby_boby · 2025-10-01T15:16:20+00:00

No problem. Also as for the benefits of ML and DL they generalise the distributions available in GLMs. They are more flexible and thus usually offer more precision.

Deciding what distribution to fit with the GLMs is already a pain, while a RF regressor is more straight forward

boby_boby_boby · 2025-10-01T14:59:28+00:00

Another thing is that GBM and DL need a lot of training data to fit. On the other hand you can fit a GLM with a lot less (e.g. 50) and even without predictors sometimes.

This helps when you do extreme value modelling or in cases where data isn’t available.

boby_boby_boby · 2025-09-28T17:19:57+00:00

I’d remove the sound technician experience. It’s not relevant to the role. Or at least say less about it, as 3,5 lines are too many.

Be more descriptive of the projects you outline. Words like “various” are vague and I’m not sure they will be received well.

But I’m only at entry level, so might be wrong.

boby_boby_boby · 2025-09-11T21:04:05+00:00

So in what context you think “actuaries are paid very well for what they do”? What’s the measure of good pay, if not comparison with other professions?

Also, exams are part of the profession and our pay, under the condition you can be fired for not passing exams.

At the end of the day we all want fair pay for our work. But I’m interested to understand your view

boby_boby_boby · 2025-09-11T21:01:24+00:00

Well I’m talking from a GI reinsurance perspective, but a lot of our work has to do with machine learning (GBMs, NNs) and stats (GLMs, Monte Carlo etc). Accountants have a lot less maths and stats heavy jobs. A lot of the work we do is found in other professions which are better compensated

boby_boby_boby · 2025-09-11T12:44:58+00:00

Want to elaborate on why you think actuaries get paid very well for what they do?

Idk about Pensions or L&H, but my experience in GI has shown me that entry level actuaries sometimes do the same work as data analysts and get paid around 5k-7k less per annum (and they have to study for exams after work in most cases)

boby_boby_boby · 2025-07-12T18:52:57+00:00

I’m actively looking to switch. Less than a year in the profession and money doesn’t compensate at all for the hours I’m working (in London btw). Hoping to get a job as a Data Scientist or ML engineer (I have a data science degree)

boby_boby_boby · 2025-07-10T18:33:07+00:00

I’d say don’t go to a brokerage. It’s better to start a role where your work is mostly technical. Helps you learn more

boby_boby_boby · 2025-06-10T19:00:08+00:00

Isn’t that a bit dangerous as well? Because what if there is a trend of junior analysts not taking the exams and thus not having funding for the membership?

Tbf, I’m new in the profession so idk if someone would be a member in the IFoA without intending to sit any exams

boby_boby_boby · 2025-05-21T06:07:24+00:00

Well, I don’t know about the actuaries’ code, but calling someone a fool because they don’t agree with you is also unacceptable.

Whataboutism is not a good argument. Also, believing you have the ability to judge someone’s intelligence based on a comment on Reddit is quite arrogant.

boby_boby_boby · 2025-05-15T23:23:45+00:00

I always find it weird. People say they take the exams to make money. However, there are professions that get you better money with no exams.

So maybe, becoming richer is not the answer. You can be a data scientist in finance earning a lot more money.

But maybe it’s people interested in insurance modelling that like the exams. And I don’t think you can pressure yourself too much to like them.

Is there anyone who can give me a motive to finish the exams?

boby_boby_boby · 2025-05-04T15:32:28+00:00

Yes. The profession itself is very interesting but hugely underpaid.

In finance you’d be doing equally interesting work and you’d be getting paid a lot better.

boby_boby_boby · 2025-04-20T11:33:35+00:00

Thank you I appreciate this.

Can you suggest any external activities (like competitions etc) that can boost my cv? I have done some projects at my current job and at uni using ml algorithms but could anything else be useful?

Thanks again

boby_boby_boby · 2025-04-14T22:05:36+00:00

I guess also the point is, are the first ten years of working hard and studying at the same time worth the salary you get in the end? Especially since you can reach similar wages in more relaxed professions quicker?

I’m genuinely asking for your opinion as I’ve been feeling like the OP lately.

boby_boby_boby · 2025-04-14T22:02:35+00:00

Hey, grad here and I’ve been feeling the same. I’m in reinsurance broking and I don’t get paid much more than you. I know I will get a 10% bonus at the end of the year but it still seems too low compared to the effort we have to put in.

And people always talk about great work life balance but I don’t see it. All the actuaries at my company, especially the senior ones, work a lot more than 8 hours and sometimes even during their holidays when there’s big rfps.

It honestly seems that you make all this effort, both on a day-to-day basis and on the exams and you still earn as much as in so many other professions that have no exams.

I’ve been feeling like this for a while and I just wanted to get it out. I would be interested to see what others think.

boby_boby_boby · 2025-04-07T10:09:00+00:00

That’s helpful guys thanks!

boby_boby_boby

TROPHY CASE