- Tags:: 🗞️Articles , Generalist vs specialist, Data team roles
- Author:: Eric Colson
- Link:: Beware the data science pin factory: The power of the full-stack data science generalist and the perils of division of labor through function | Stitch Fix Technology – Multithreaded
- Source date:: 2019-03-11
- Finished date:: 2021-11-07
…we should not be optimizing our data science teams for productivity gains; that is what you do when you know what it is you’re producing (…) But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products (…), and more can’t be designed up-front.
Que esta idea también está en el software, BTW.
3. It narrows context. Division of labor can artificially limit learning by rewarding people for staying in their lane. For example, the research scientist who is relegated to stay within her function will focus her energy towards experimenting with different types algorithms: gradient boosting, neural nets, random forest, and so on. To be sure, good algorithm choices could lead to incremental improvements. But there is usually far more to gain from other activities like integrating new data sources. Similarly, she may develop a model that exhausts every bit of explanatory power inherent to the data. Yet, her biggest opportunity may lie in changing the objective function or relaxing certain constraints. This is hard to see or do when her job function is limited. Since the research scientist is specialized in optimizing algorithms, she’s far less likely to pursue anything else, even when it carries outsized benefits.