(Inspired by a bunch of recent blog posts I’ve come across with titles like: “The Next Generation Data Scientist” and “The 3rd Wave Data Scientist”, etc…)

​

Type I – the olympian deity: Works as part of the core ML or research team of a FAANG or for an academic team at a major university. Has multiple papers at NeurIPS or a similar status conference or publication. Wouldn’t be able to recognize domain knowledge even if it punched her/him in the face. Envied by all others in the data science / machine learning community, but is actually miserable because she/he hasn’t received the Turing award or gotten a theorem or class of neural networks named after them. Is this close to giving up and becoming a tenured lecturer for linear algebra 101 at a community college.

Type II – the demigod: Works for an applied ML/product team at a FAANG or an up and coming very promising startup. Actually knows how to program in C++, understands dynamic polymorphism, and can solve Leetcode hard problems in their sleep. Also the envy of all others in the data science / machine learning community, but is actually miserable because they have to work 85 hours a week, and the awesome framework they contributed major chunks to 2 years ago is no longer fashionable and has been superseded by another framework. Also miserable because they could have worked for a startup and been billionaires at the age of 32, but instead are making a measly 250K a year in a city where a suburban one bedroom costs 1.5M.

Type III – the grumpy old hand: In their late 40s/50s, was around during the previous Neural Networks hype cycle, and worked on them before the Tensorflow core dev team was even born. Has survived at least one A.I. Winter. Works for Boeing or Walmart or something like that. Doesn’t know what StackOverflow is, and doesn’t need it since she/he learned how to code back when people had those huge “The Java Bible” and “The Unix Bible” reference volumes on their desk, and therefore can actually figure stuff out from the documentation. Is almost as good a coder as the demigod, and almost as good a mathematician as the olympian, and beats them both in domain knowledge. But she/he has no idea what GitHub is, never had to spin up a docker pod or run something on a GPU, and haven’t done an interview in 27 years. The last time they did, interviews were 45 minute affairs conducted over the phone. They are not really the envy of anyone in the data science / machine learning community, except for the fact that they have accrued 7 weeks of PTO by this point in their career. They are miserable because they know they are stuck in their current role, ageism is a definitely a thing, and they will likely be the first to go come the next economic downturn.

Type IV – the hipster data scientist: young, very likely a millennial or even Gen Z, although might be a Gen Xer who is in very good shape and has tattoos. Recent college graduate or somehow managed to transition from a marketing or sales role into data science after completing a couple of classes on Udemy. Has a huge social media presence, and their LinkedIn profile picture is one of them at a podium with a mic or giving a TEDx talk. Has produced several blog posts and/or podcasts. Doesn’t know any languages besides R. Doesn’t know what a partial derivative is, and freaks out whenever they see an integral, but is still very good at explaining and simplifying concepts, hence always has the attention of the business stakeholders. Says things like “Cross-Validation is fun”, “And I love boosting” with a straight face. Usually works for a small to mid-level company, but occasionally manages to land a role at a FAANG, after which they develop weapons grade levels of obnoxiousness. Not so much the envy of all others in the data science / machine learning community. More like the object of lust of all others in the data science / machine learning community. Is miserable because they are not Mark Zuckerberg.

Type V – the overseas data scientist: As good as the olympian, the demigod, and the grumpy old hand combined, but nobody takes them seriously because of their skin color and very thick accent. Their career is additionally hampered by their cultural aversion to self-promotion and BS artistry which comes so naturally to many Westerners. Can do EDA, prototyping, production deployment, A/B testing, performance testing, and devOps all in one day, yet still somehow manages to be out of the office by 16:45 (they also don’t show up until 9:45 in the morning). Is the reason why the hipster data scientist is able to get away with so little real work. Is the envy of all others in the data science / machine learning community, because they know that the overseas data scientist will be the last one to be let go in case of a $#!tst0rm, since they do the majority of the work on the team despite having the lowest salary. Is also the envy of everyone else, because at 35, they already own a home and have two teenage kids who are getting straight A+s in school. Is none the less miserable because their H1b might get revoked any day now.

Type VI – the stealth data scientist: Very smart dev or TPM, who never actually used the title ‘data scientist’ or even ‘machine learning engineer’ (they might even sneer at those titles). A mix of the hipster, the demigod and the overseas data scientist in terms of personality and demographics, who works mainly on infrastructure or platform stuff, or maybe on the web portal team, but has also prototyped and deployed more than one regression or clustering model to production, without ever having taken a single machine learning or stats class. Has no idea who Andrew Ng is, and thinks A.I. is mainly about robotics and the Turing test. Masters Shell, Java, Scala, Kotlin, Node.js and PL/SQL, and can shift between on-prem and cloud systems at will. Thinks Python is a joke, and is floored every time somebody says they are a Python expert even though they don’t know OOP. Is not so much envied, as they are feared by the rest of the data science and machine learning community, because the stealth is unphased by any of the Deep Learning hype, and once the AutoML frameworks finally mature, they know that the stealth will make the rest of them redundant. Is not miserable at all, because the stealth knows that they will be around long after the whole DS/ML hype died down, and don’t really care because they will be Sr. Director or VP of Engineering by then anyway.