>However, this does not mean competition winners cannot develop and learn this - they have already demonstrated their creativity and tenacity in one domain (app

>However, this does not mean competition winners cannot develop and learn this - they have already demonstrated their creativity and tenacity in one domain (applied machine learning), and this carries over nicely to other domains from our experience. Strongly agreed. I actually got into the field through a company's data mining contest (pre-kaggle). I think people who are strong at building predictive models are great candidates for data sciences. But it took years of work experience after doing my graduate degree in machine learning to get to a point where I'm comfortable calling myself a decent data scientist. It's easy to think model-building is the only important skill-set, but data and models don't exist in a vacuum; a more holistic view of where your data comes from and how your work will be used is essential. This excellent netflix blog entry illustrates what I'm saying quite well, I think. http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html http://techblog.netflix.com/2012/04/netflix-recommendations-... They make two points that illustrate the divide between a contest and the day-to-day work of a data scientist: * The winning model was not usable in production. Netflix had to gut the 100+ model ensemble to a much simpler 2 model ensemble * Business needs change, the question they were trying to answer changed from the start of the contest