Data science jobs in finance: "super smart" people wasting talent
Data science is a hot ticket in finance right now. From investment banks to hedge funds (JPMorgan in particular), everyone wants data specialists and everyone's willing to pay big money. However, there is a pervasive issue affecting the sector that seriously limits the quality of data science talent: a lack of fundamental data science knowledge.
This is a point contended by Marco Jean Aboav, a quant-turned-portfolio-manager who now acts as co-founder of indexing infrastructure fintech Etna Research. He has released a comprehensive guide for data scientists covering both technical and cultural factors and says that candidates with fundamental knowledge make up "essentially less than 5% of candidates."
"The industry is shifting towards hard skill data science," Aboav says, "you need to become more and more like a developer than just a scripting guy." An unfortunate trend in data science today is people "showing off the most advanced techniques without demonstrating any core knowledge."
Aboav says, "If you want to be a successful data scientist, you need to think like a computer scientist." However, especially in US quantitative finance, there is a tendency to recruit from finance or economics backgrounds. Aboav says it's better to hire "someone that’s super good at the basics and provide ad-hoc training, you get much more out of that person."
Technical knowledge is, in itself, not what's most important; it's an understanding of technical functions within a production environment that truly prove a candidate's worth. "Data scientists can be super smart, but if no lines of their code will go into production, it's a waste of human talent."
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