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There is an interesting thread I was reading here, discussing the "future of statistics." This got me thinking about the future careers which may be in-demand and future careers which may be created for statisticians.

Originally, academia and mathematics were the careers of choice. Then, Wall Street adapted probability and statistics through fixed income products, and later algorithmic trading. After that, Silicon Valley scooped up the statistically-inclined for machine learning and algorithm development. This shift in demand was (relatively) rapid, ocurring over the past 50 years or so, after the development of Black-Scholes-Merton model in 1973. Since then, demand for an understanding of advanced statistics has skyrocketed, and many careers were created from this new demand. It is likely that many new types of jobs will be created in the near-future, and that some antiquated industries will evolve to incorporate the use of statistics.

In your expert opinion, where do you think most statisticians will be working in 15-20 years from now? What sorts of new jobs will be created to involve the analytically-minded?

ERT
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    Where? In India and China, that's where most of them will be working :) – Aksakal Jul 24 '18 at 20:53
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    @Aksakal Haha! I actually disagree with your opinion on that point, but I like the answer! – ERT Jul 24 '18 at 20:59
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    The answer is: they'll be following the money, just like all professions. Right now for some reason that money is in AI and Machine Learning. With that being said, it is also more important than ever for there to be well-grounded statisticians working with companies to ensure data-driven decisions are made, especially when it comes to testing new product ideas. – Alex R. Jul 24 '18 at 21:21
  • @AlexR. So, can you predict *where* that money will be headed!? – ERT Jul 24 '18 at 21:27
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    Money must be moving into food and war – Aksakal Jul 24 '18 at 21:42

1 Answers1

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You quote from the quantitative finance industry, and coincidentally I worked in QF so I think my experience should be useful.

  1. In the past, we call the QF statisticians as "quant", now we call them "data scientist". Classical "quant" knowledge include C++ programming, but nowadays companies prefer Python/Ruby/Scalar/R. C++ programming would be better reserved for professional software engineers.
  2. Indeed, there is little market for statistical development in new QF models. Companies have money for a FINCAD license, so why should they hire a quant model developer? Black Scholes is done. The exotic option models once dominated the model disappeared after the credit crisis, as the financial industry prefer simpler vanilla products.

Don't worry ... there're lot's of jobs on the market. Open up your favourite career website, and you will see tons of data scientist positions. Good statistics knowledge is important for data science.

The future for statisticians is bright, in finance and also many other areas. Strong knowledge in statistics and computing will make you a strong candidate. Candidates with only programming experience won't be a good data scientist.

Have you scanned our questions? We have tons of questions asked by programmers, they could write R-scripts but they wouldn't have any idea how to interpret the results. This site is flooded by unqualified software programmers, and you don't want to be one of them.

Distancing yourself from them will make you useful for jobs like:

  • Machine learning development and validation
  • Big data analytics
  • Biostatistics
  • Precision medicine
  • Automatic driving
  • Mobile apps analytics
  • many more...

Future statisticians need good computing skills.

SmallChess
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