Subject archive for "data-scientists," page 10

This post describes our engineering philosophy of building an “open” product, i.e., one that supports existing tools and libraries, rather than building our own custom version of existing functionality. Aside from letting our developers be more productive, we’ve found this approach makes our users much more productive — especially power users, who are especially important to us.

Many analytics managers we know describe their greatest challenge as recruiting data scientists. They often blame the problem on too much demand now that “data scientist” is the “hottest job of the 21st century.”

Direct email outreach is one of our sales channels: we find data scientists working at interesting companies and email them. We've refined our template through a few iterations and A/B tests to the point where we get a response rate of over 5%, which seems reasonable. But we also get some perplexing responses. I wanted to share some of them, not as sour grapes, but because I think they shed light on some of the challenges involved selling enterprise data science software. Just to be clear, I am not complaining; I'm sharing -- for two reasons. First, maybe people will have suggestions. Second, this might help others trying to do enterprise sales.