What meaningful careers exist in data science (stats/ML/optimization)? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.
What meaningful careers exist in data science (stats/ML/optimization)? This is a great question, coming from academia myself, I can relate to it. It really depends on what you personally find meaningful, and what your goals are. Would you be happiest doing cutting edge machine learning that impacts millions of people? Or perhaps you are most concerned with doing something that has some kind of charitable cause? (These aren’t mutually exclusive, by the way).
The truth is that the use of machine learning is exploding in a very large number of industries. In almost every area of labor, there are parts that can be made more efficiently, cost-effectively, or just generally better with machine learning. Areas that were once done without machine learning will now be re-invented with machine learning.
A few other random areas where machine learning is really taking over:
Search and discovery: this includes the ranking algorithms that Google uses to decide what results to show you when you enter a query, as well as other things like search autocomplete, and the complex algorithms that Facebook uses to show customized results in your news feed.
Recommendation algorithms: Basic permutations of a well-known algorithm called collaborative filtering allow Netflix, Amazon, and many other consumer companies to provide sensible and personalized recommendations based on previously observed user behavior.
Fraud detection: typically used by financial companies to detect when credit cards and the like are used fraudulently.
Optimization algorithms: Uber/Lyft/Instacart must use complex optimization strategies to route drivers and shoppers to the best destination given evolving demands. It's a very interesting area.
Image recognition: With the advent of convolutional neural networks, there is a lot that is possible that wasn’t possible before. For example, this technology has enabled self-driving cars, and the ability to search photos for specific objects by name, and grouping pictures of the same individual together regardless of age (both features in Google Photos).
Business Intelligence: companies like Quid (full disclosure: previous employer) are changing the way people think about the consulting business. It seems like only a matter of time before how people consume and research information will be truly altered (for the better) with interesting ways of looking at a lot of information at once, enabled by interesting unsupervised machine learning strategies.
Healthcare: machine learning will eventually completely change the way healthcare services are delivered. You have companies likecompletely upending how radiologists screen X-rays, and in some cases, machine learning can do this better than humans. Its widely agreed that the way AI/machine learning is headed, toward providing strategic ways that human intelligence can be augmented and used more effectively, not to replacing human intelligence itself (at least in any short to medium term horizon), and this is a great example. See here for an interesting example of cataract management/detection: See also: .
Now, my last point — as far as purpose goes in your career. I’d add one more thing, which is that, as compared to academia, where career transitions are very linear: PhD -> postdoc -> professor -> tenure, there is more flexibility to define your own customized route in an industry setting. The way I see it, this is actually to your advantage, and allows you to shift gears as you discover things that really excite you. Another way to think about it is that, some jobs can just be about learning, rather than your main passion, which will nevertheless also play a role in allowing you to get where you where you want to go in the long run (whether its to start a non-profile, start your own company, or become an ML executive, etc). The sky is really the limit.