After reading most of the comments I can try to provide a different perspective.
I am a Director of Data Science and Software Engineering for a mid sized firm (~1000 employees and $150-200MM revenue). I started with a Finance degree then shifted into an analysis position at a FAANG (lots of excel, SQL, learning how to query big data). This eventually led to learning more about tech (python, AWS cloud stack, messaging queues) and after 8 years in the industry giving me enough experience to manage teams of data scientists, software engineers and data analysts.
Although it is so important to know all the software engineering stack, many companies will benefit from simple business intelligence and data analyst roles. I guess my recommendation is to also keep an open mind in looking for these types of roles in the market (data analyst, business intelligence engineer), because given your desire to learn and existing background, its clear you can make a big impact in those companies as well. And it will be much less competitive than traditional CS crowd.
I think this is great advice but there’s an important caveat which is career goals in AI / ML.
A lot of companies that say “data scientist” when they really mean “spreadsheet analyst” are places to avoid if you have career aspirations in ML. In the worst cases it can be a bait and switch (very common) to get overqualified people to babysit rudimentary analytics. Especially avoid places that might do this to pad their staff for any type of acqui-hire or investor signalling reasons, because your career goals will not be acknowledged.
In the best cases, it can be some befuddled IT manager who vaguely thinks they need “AI” but really they don’t have projects that would actually benefit from it. They might be sympathetic to your dissatisfaction in the reality of the job, but will have little power to do anything about it.
Somewhere inbetween is another very frustrating case: situations where the business or product clearly can materially benefit from “real” machine learning, and from the perspective of making customers happy & making money it’s a no brainer to invest time to research implementations, but risk averse management, often with no ability to gain an understanding of the benefits of investing in machine learning, or who want to act as credit / politics gate-keepers for an existing system, puts the brakes on it and retasks you on things that just waste your talent.
Not disagreeing with you (not informed enough to), but top tech company by what measure? Market valuation? Impact of products/services in 2018/2019? Workplace rating?
Okay, but repeatedly making this point in this thread is derailing conversation. Does it really matter that much? Were you unaware of the parent comment's point being made because MSFT isn't represented?
I am a Director of Data Science and Software Engineering for a mid sized firm (~1000 employees and $150-200MM revenue). I started with a Finance degree then shifted into an analysis position at a FAANG (lots of excel, SQL, learning how to query big data). This eventually led to learning more about tech (python, AWS cloud stack, messaging queues) and after 8 years in the industry giving me enough experience to manage teams of data scientists, software engineers and data analysts.
Although it is so important to know all the software engineering stack, many companies will benefit from simple business intelligence and data analyst roles. I guess my recommendation is to also keep an open mind in looking for these types of roles in the market (data analyst, business intelligence engineer), because given your desire to learn and existing background, its clear you can make a big impact in those companies as well. And it will be much less competitive than traditional CS crowd.
Some food for thought