The Hard Questions of Hiring For Machine Learning Posted on : Dec 04 - 2018

I’ve been thinking a lot about hiring for the machine learning specialization lately. It’s no surprise. New data is emerging almost daily about the rise of machine learning, artificial intelligence and deep learning in software design. Just recently, IDC reported that spending on cognitive and artificial intelligence (AI) systems is set to accelerate well beyond original forecasts by more than 300% in the next five years. Even a survey at my company revealed that almost two-third of enterprises are experimenting with AI.

This means we’re always on the lookout for machine learning talent to work on our service-centric AIOps platform, and it’s like panning for gold. The New York Times reported that there are about 10,000 people in the world with the skills to handle the hardest problems in AI. Of course, we’re not just looking for one of these unicorns. We need teams of them to work on building solutions with neural network architecture, Naive Bayes Classifications, and Singular Value Decomposition. That means we need experts in data science, who can use data to validate models, and engineering, who can code the mathematics into the software. Simple, right? Not really. Here’s how we attack this problem fundamentally, with a few of our tried-and-true interview questions that help us find the intelligent minds behind artificial intelligence.

Data: The Ghost In the Machine

To start with, it’s critical for our candidates to understand the value of data in building artificial intelligence. In fact, as technology gets more complex with the rise of discrete, ephemeral workloads like containers, microservices, cloud instances and serverless computing, the amount of data that can potentially be fed into an artificial intelligence solution is multiplying exponentially. That means a scalable solution must be able to handle large quantities of data quickly and accurately.

Data is what underlies our platform and it’s at the core of any algorithm built to solve a problem, so a deep understanding of statistical methods and data manipulation is key. Ideally, a candidate would come to us with an advanced degree in these fundamentals, but in the absence of that, we ask some questions to get at the heart of their allegiance to data, including:

How do you prepare data for machine learning and how do you convert different objects into something the model can learn from? View More