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I currently work as a Big Data Administrator at a large financial services company. A position has opened for a data analyst role in an anti-moneylaundering team focused on cryptocurrency. I am in a dilemma whether accepting the offer (not that great of a salary bump) or remaining in my current role which I have a promotion lined up this fall. Although I do have a Hadoop cluster where I can study and learn, I feel like my current role is more like a sys admin's (OS patching, configuration mgmt, etc.). Just wanted to know your thoughts on what seems better as a stepping stone to a data scientist role.

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To me, it sounds like you don't see much future in your current role, combined with a sense of boredom/lack of challenges - so it would seem that you're looking for a way to grow your career.  Depending on your relationship with your mentor at work, be honest with them - tell them that you're looking for new challenges and are seeking their advice.  In some senses, they may know you better than you know yourself - hopefully, you have someone in this role that can help you with practical advice on whether or not this other position would be a good fit for you.

Speaking of the new role in particular, I think it's a growing field - but with much less certainty around job security, needed skills, level of frustration in getting things done.  If your company realizes the risk and is putting resources to it, there are many different toolsets, approaches (serious machine learning and mathematical approaches to detecting fraud as it's happening - think the last time you took a trip and your credit card was rejected at the gas pump.  How did they do that?).  So, anti-money laundering looks for pattern matching, associations well beyond your normal data sources (in the case of the gas pump, a geospatial element to determine how far away from home you are), relationships that are both intrinsic and not obvious at all.

So, if you like constantly shifting challenges, a task that is never done, an ever-evolving way of managing data and business rules - then go for it.




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