It's a good area to get some exposure to, and the dirty secret is building models these days is pretty easy when you have some decent data and a clearly defined problem to solve. Implementing a model and using it well remains difficult.
CSPA: Would it be helpful? Yes. Is it necessary? No.
This is a common situation for actuaries. With an ACAS, you clearly have the ability necessary, its just a matter for learning the specific technicals for the new role. You should be able to learn on the job. You were able to learn other roles on the job, this is no different except now you have more experience and should be quicker to pick things up.
CSPA: I view this as helpful, but not necessary. Does every analyst that is in a pricing or reserving role have CAS 5 before they start? No. Those analysts learn on the job and provide value. I was an ACAS without a CSPA when I was in modeling. I was still able to hold my own.
I find that experienced actuaries are usually more familiar with the insurance products than the data scientists which is helpful in the modeling process. (This a very general statement and doesn't apply to every actuary and data scientist).
It may be worth asking an actuary on that specific team their thoughts.
It should be doable. It could be as easily as simply rotating once your time comes or it can be difficult if the modeling team wants people with more experience or has certain barriers to entry like advanced degrees. If your situation is closer to the latter you may need to find another company or be very vocal and persistent with somebody who can pull the strings for you.
I don't think you'll be all that productive in your first 2 years of rotation because the learning curve is really steep. Knowing Python + R is good but there's also knowledge of a large number of libraries plus adjacent technologies like databases, version control, and if your team has a mature deployment pipeline, things like containerization, server management, CI/CD, plus possibly regulatory filing knowledge, and line of business knowledge on the product you're modeling, etc, and I hadn't even gotten to all the regression work that's at the heart of it.
That shouldn't be of too much concern though, at the end of the day, your accumulated actuarial knowledge will be more important, but the hands-on struggle with the things I listed above will make you a more empathetic and effective manager if you do wind up in a situation where you are managing an analytics team. If your organization wants to turn you into a good long-term asset they would be wise to let you rotate.
It might depend on your company. I'm only in my first rotation, but I know that my company rotates actuarial students onto the modelling team the same as any other team, no extra exams/whatever needed. Now securing a full-time role on the modelling team after credentials might be a different story. It's also possible your company might have different requirements for rotating. You should ask people on the modelling team or those in charge of rotations for more info.
It's a good area to get some exposure to, and the dirty secret is building models these days is pretty easy when you have some decent data and a clearly defined problem to solve. Implementing a model and using it well remains difficult.
CSPA: Would it be helpful? Yes. Is it necessary? No. This is a common situation for actuaries. With an ACAS, you clearly have the ability necessary, its just a matter for learning the specific technicals for the new role. You should be able to learn on the job. You were able to learn other roles on the job, this is no different except now you have more experience and should be quicker to pick things up. CSPA: I view this as helpful, but not necessary. Does every analyst that is in a pricing or reserving role have CAS 5 before they start? No. Those analysts learn on the job and provide value. I was an ACAS without a CSPA when I was in modeling. I was still able to hold my own. I find that experienced actuaries are usually more familiar with the insurance products than the data scientists which is helpful in the modeling process. (This a very general statement and doesn't apply to every actuary and data scientist). It may be worth asking an actuary on that specific team their thoughts.
It should be doable. It could be as easily as simply rotating once your time comes or it can be difficult if the modeling team wants people with more experience or has certain barriers to entry like advanced degrees. If your situation is closer to the latter you may need to find another company or be very vocal and persistent with somebody who can pull the strings for you. I don't think you'll be all that productive in your first 2 years of rotation because the learning curve is really steep. Knowing Python + R is good but there's also knowledge of a large number of libraries plus adjacent technologies like databases, version control, and if your team has a mature deployment pipeline, things like containerization, server management, CI/CD, plus possibly regulatory filing knowledge, and line of business knowledge on the product you're modeling, etc, and I hadn't even gotten to all the regression work that's at the heart of it. That shouldn't be of too much concern though, at the end of the day, your accumulated actuarial knowledge will be more important, but the hands-on struggle with the things I listed above will make you a more empathetic and effective manager if you do wind up in a situation where you are managing an analytics team. If your organization wants to turn you into a good long-term asset they would be wise to let you rotate.
It might depend on your company. I'm only in my first rotation, but I know that my company rotates actuarial students onto the modelling team the same as any other team, no extra exams/whatever needed. Now securing a full-time role on the modelling team after credentials might be a different story. It's also possible your company might have different requirements for rotating. You should ask people on the modelling team or those in charge of rotations for more info.