I recently read an article by Daniel Tunkelang entitled Data Scientists: Generalists or Specialists? and it resonated with me. I’ve been involved with hiring data scientists for some time now and I also get a lot of recruiters contacting me about various data science jobs. My general observation is that when companies search for data scientists, they tend to use the equation (Machine Learning = Data Science), and tend to play down all the other skills that make up data science, such as creativity, critical thinking, data preparation etc.
Generalists add more value than specialists in a company’s early days, since you’re building most of your product from scratch and something is better than nothing. Your first classifier doesn’t have to use deep learning to achieve game-changing results. Nor does your first recommender system need to use gradient-boosted decision trees. And a simple t-test will probably serve your A/B testing needs.
Generalists hit a wall as your products mature: they’re great at developing the first version of a data product, but they don’t necessarily know how to improve it. In contrast, machine learning specialists can replace naive algorithms with better ones and continuously tune their systems. At this stage in a company’s growth, specialists help you squeeze additional opportunity from existing systems. If you’re a Google or Amazon, those incremental improvements represent phenomenal value.
So, should you hire generalists or specialists? It really does depend—and the largest factor in your decision should be your company’s stage of maturity. But if you’re still not sure, then I suggest you favor generalists, especially if your company is still in a stage of rapid growth. Your problems are probably not as specialized as you think, and hiring generalists reduces your risk. Plus, hiring generalists allows you to give them the opportunity to learn specialized skills on the job. Everybody wins.
Read the complete post here on O’Reilly.com. What needs to be noted here is that companies will need more specific skills as their analytics mature and evolve, however in the beginning creativity, competence and critical thinking are most likely the most important skills. I tend to agree with a lot of what Tunkelang writes, and I do get the sense that a lot of hiring managers believe their projects are a lot more mature and advanced than they really are. Thoughts?