Model validation vs non FAANG or fortune 500 data science

Hi guys,

I'm graduating from an MSc program in stats in 2 months and about to get my first first "real" job, I went directly for my master's after my bachelor in stats. I don't know any senior people in either quant finance or data science and I'm the first in my family to go to graduate school so I'd would love to hear your opinions. I have done all my internships in the financial industry (investment banking and equity research) and realised that both required too long hours and the work was not stimulating, I want more out of life than doing Powerpoint presentations 14 hours per day. It might be naive to say but my end goal is to have an upper middle class lifestyle in 20 years (owning a house, a car etc) with an interesting job and good work-life-balance where I get to leverage my coding and stats skills. Quant finance naturally appeals to me more than the tech or consulting industry. I am also quite extroverted so I don't want a job with no interaction with other people. I am deciding between two jobs.

The first job is a data scientist job at a small consulting firm of 25 ppl outside of the financial sector with many young people and a startup atmosphere. I would be one out of two data scientist in an organisation of business and sales people. I would mostly work with R, Python and SQL to automate and analyse data flows from APIs, discuss and create new data products to clients. The job would include some machine learning and to create dashboards. Now, I am a little bit worried to be in a team of just 2 data scientists during the first stage of my career, it would more or less mean that I have no one to learn from. However, the company says that it would allow to potentially get to a more senior position faster and that I will be compensated well in the future if I do a good job. I did not get any technical questions during the interview which makes me a little suspicious, they mostly just talked about their vision for the future and the importance of making their organisation more data driven. Data scientist is such a general title, I suspect that the role might just be another analyst role that they renamed data scientist in order to attract better talent. Most of the people in the organisation come from a business background.

The second is a credit risk model validation job for a major bank. The team is responsible for the validation of all credit risk models for the bank, they only work with SAS. The team is very senior and people seem to be very smart, 40% have a phd and I would be the only junior person in the team. I got a very serious impression of the job during the interviews with both technical and soft questions. I even got interviewed by an alumni from my MSc and the head of the team has previously had many quant roles within the bank. I'm just worried that I would be stuck with SAS and risk validation if I go for that route.

In terms of starting salary, they are more or less the same.

So I have some questions.

1. How important is it to be around senior people at the beginning of your career? I'm thinking that being in a team with very senior people would mean more competition but also allow me to learn more.

2. Hard to say ofc, but which route would in the end pay more? Would model validation for a major bank or "lower tier" (not FAANG or fortune 500) data science pay more? The salary statistics online on places like Glassdoor suggests that data science pays more but I suspect that the figures are biased since data scientists tend to work in more expensive locations. I also don't think that I am talented enough to break into FAANG data science even in the future.

3. If I go for credit risk, is it hard to transition elsewhere if I don't like it? Since data science is so hyped right now, I feel like even if I don't like the job at the small consulting firm, as long as I have the title "data scientist" it would be easy for me to find a new job both inside and outside finance. Whereas it would be much harder as a model validator. R, Python and SQL also seems to be way more transferrable skills than SAS.

4. Are the skills that you acquire from model validation in high demand? I might want to try to work abroad at some point. Would that be hard as a validator? It seems to be pretty easy as a data scientist.

5. Which one is the most reasonable to go for in your opinion? Generally I am a little bit more excited about the validation job but I am also thinking that it might be stupid to not go into data science if I have the opportunity since it is so hyped right now and that so many people say that "it's the future".

Sry for such a long post, I'd love to hear your thoughts. It so hard to make such life determining decision after 4 hours of interviews...