Paul from Bayes Impact here. I appreciate the sentiment, though in all respect it does seem like most of your concerns are addressed on the website, either on the fellowship page or in the others.
> unless you provide a little more information about what these "hard" problems are
The second paragraph does go briefly over the problems we are currently working on (granted, not in much detail for the sake of brevity, but enough to give an idea of what type of challenges they are). There is a little bit more information on the front page, but granted since we started Bayes Impact two months ago we haven't been able to put as much work into the website content as we'd like to.
> Honestly it reads like your offering basic in training in a a random selection of tools
This is simply not the case -- while their level of experience varies, our current fellows actually comprise some well-established data scientists in their own right. It is precisely because the problems worth solving are tough to solve that we need to round up talented individuals who are able to commit to working on social impact projects full-time and pair them up with industry and domain experts who have the domain knowledge but may not have the time.
They each bring their own set of skills -- for example, someone who built Lyft's grid optimization system might be uniquely suited to help save lives by improving ambulance and fire truck dispatch and reducing average emergency response times.
> and then hoping some non profits present a problem with nice clean data that can be solved through application of a few methods from scikit.learn
This is precisely the point of Bayes Impact and why a longer engagement model such as fellowships is needed in the space (most current data science for social good organizations work on a volunteer basis model), so we have the time to build these longer relationships with nonprofits to leverage data science even in cases where data is messy or sensitive. We go a little bit more in-depth about it on our article here: http://blog.bayesimpact.org/blog/the-bayes-impact-mission/
> Worse 4-6 months might not even be enough time to formulate a problem that needs a solution
This is why they're not 4-6 months, but typically 6-12. We do have a pilot 3 month program in the summer for problems that are comparatively easier to work on.
> and then hoping some non profits present a problem with nice clean data that can be solved through application of a few methods from scikit.learn
This is why we have a fellowship application page and not a project application page -- we actually tend to identify and scope projects ourselves.
On that note though, I want to point out there is no need to be so overly dismissive of the work nonprofit and civic organizations have been doing in collecting and storing clean data. For example, most fire departments we talked to had surprisingly good data, and some such as the Fire Department of New York had even started initiatives of their own to use data science to improve their processes. For example, by integrating building permit data with their own systems, they've been able to direct inspectors where fire were predicted to be more likely to occur.
One direction we've been headed towards is seeking these data-educated organizations to create pilot projects, then use the results of these as a basis to export these solutions in similar institutions whose data practices may not be as good. In that end, we are helped by some data engineers from companies like Splunk or Cloudera so we do believe in working with these organizations in the long run to bring them up to speed. This is precisely the problem we're trying to solve with our model!
> For the record I work for a non profit analyzing complex diseases
Then you might be interested in the project we are doing on Parkinson's with the Michael J. Fox Foundation! Feel free to email me for more details.