In other words, who has the greatest need for data scientists and data analysts and would be willing to hire people virtually? And what kind of projects do you think make sense in such a context?
There are three major challenges with the scenario you describe:
1) the data is usually not in a format that is ready to be chewed upon (less critical)
2) The data-science tasks are usually not well-defined by the people who need them (very critical)
3) The process tends to be iterative and not on-shot.
The only successful situations I'm aware of that is close to the one you describe is competitions/benchmarks where the task is crystal-clear and the data is ready-made (like the Netflix one or many others run in the research community and by the government).
In these competitions, issues (1) and (2) are addressed, and you may hire a person so that they can iterate on it and continue the work (issue 3).
In certain domains and projects there's less iteration needed, so if the problem to be solved is well-defined and data is well-prepared, it can be done successfully. I've been in several situations where I "ordered" a data-driven algorithm and plugged it in a live system.
Answered 11 years ago
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