Blog archive, page 88

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.”

We recently caught up with Robert Woolfson - Ph.D. in Computer Science from Manchester University, now heading up the data science team at Wix.com.

There's buzz everywhere these days about exciting applications of data science, especially related to machine learning. While the majority of the media attention focuses on big names and sexy topics — e.g., the Google Brain, image classification, speech recognition — we are fortunate to work with companies every day that are doing fascinating work with data science tools. Although sometimes more mundane, many of their use cases have more real-world impact.

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.

This is a guest post by Dan Morris. The interactive dashboard and the code are also available.