Is your data out of shape? Why is it that you can never find the information you need? Surely you’re familiar with that sinking feeling. When you see a glaring error in the numbers, but you have no idea where it’s coming from. Your data lake is bloated beyond belief, so your team doesn’t know where to start looking. You’re now in panic mode. A looming report deadline, a shortly due presentation. How are you going to fix things in short order and maintain your credibility? Continue reading
I often get asked what industry trends excite me the most. Without doubt, it has to be the surge in amazing technologies that democratize solutions to complex data problems. Many of these are open source, or available on cloud platforms at the click of a button. This was unthinkable as recently as five years ago. I think it’s fantastic that all industries can now profit from the game changing work done by heavyweights like Google, Facebook and Amazon.
So where’s the problem?
Alan Turing a pioneering recruiter? You wouldn’t normally think of the father of modern computer science in those terms. It’s more natural to associate him with conversations on Artificial Intelligence – after all, he shaped our understanding of how computers solve problems, and how their ability differs from that of human intellect.
Where does recruiting fit in? Continue reading
In my last post, we saw how chasing unicorn data scientists can be an exercise in frustration. I made a case for providing data science training to your current team members who have the right domain knowledge and aptitude. But what do you do when you absolutely need to expand your team? How do you hire while competing against offers from tech gorillas like Google and Facebook?
In my conversations with companies looking to hire data scientists, I hear a recurring theme – “It’s so hard to find the right combination of industry background and technical chops.” Or. “We found the perfect fit. But there was no way we could match what Google / Facebook was offering, so we lost out. How do we build an effective team when the competition is so white hot?”
If you follow popular data science sites, you’ll notice an amusing trend. What do you think was the most popular post on KDnuggets in January? It wasn’t about the latest deep learning tools, or about Google’s recently released TensorFlow. It was: “20 Questions to Detect Fake Data Scientists”! The post clearly hit a nerve – I’ve seen similar questions crop up on many other forums. If you read some of the responses, you’ll see that people get very worked up about what makes a “real data scientist”. Continue reading