Simon Hughes, Chief Data Scientist
Growing up in the ‘80s and ‘90s with movies such as Terminator 2, Aliens and Blade Runner, I became fascinated with robots and artificial intelligence (AI) from a young age. As a teenager I decided that a true AI would need to learn by experiencing the world as a person does, rather than pre-programmed like you see in movies; that idea fascinated me.
I originally obtained a biology degree, but after graduating and having access to my first computer, I pursued a master’s degree in computer science. For my master’s thesis, I had my first true exposure to machine learning, working on a case based reasoning system for predicting potential offenders for burglaries based on their past MOs.
After graduating I worked for a number of years as a software developer for various companies, which included a long stint in finance (hedge funds, black box trading companies, and so on). Even as I wrote APIs, and built order-processing systems and MVC websites, I was always known as the database guy; I’d usually be the person who wrote the bulk of the database logic or fixed some slow query. (Many a happy hour was spent optimizing SQL Server queries.)
Throughout this period in my career, however, I never got to work on really interesting problems such as the ones I encountered while completing my master’s degree. On my wife’s insistence, I enrolled in a part-time Ph.D. program in computer science, specializing in artificial intelligence. Not long into this program, I took a course on machine learning, and found it far more interesting than the more rules-focused classical AI, which seemed very limited to me. A couple of years into the program, I started to become aware of the Big Data revolution, and heard the term “data scientist.” Realizing I could potentially get paid to do machine learning for a living, I started to scout around for data-science positions.
At the time I lived near Chicago, where there weren’t many employers looking for data scientists. When I did manage to land an interview, they wanted more of a mathematician than a machine-learning specialist. So I decided to do some networking. I met a person at a party who told me of a local machine learning meetup organized by his company (a large .com); I gave a couple of talks there, one on genetic programming and another on deep learning, which started to get me noticed in the group.
I would always bug the meetup’s organizer about possible positions in the area, and one month I got lucky when he bumped into my current boss and knew he was hiring. And that was how I ended up here. I am still pursuing my Ph.D. by the way, and also co-teach a class on data science at my current institution.
Yuri Bykov, Director of Data Science
Data science is multidisciplinary. That fact, depending on your viewpoint, can be either a deterrent or an open door (or rather, open doors). With so many different subfields involved—mathematics, statistics, data engineering, computer science, machine learning, computational linguistics and visualization, to name just a few—people from different backgrounds can enter data science.
I spent over a decade in business intelligence and data analytics, building data pipelines, star schemas, OLAP cubes, dashboards and reports. While it was an interesting job (not to diminish the value of BI), there was so much more we could do with data. Fortunately, as the organization matured, along with some disruptive changes in the competitive landscape, the demand for building data-driven products and services grew exponentially. That’s when the [Dice] data science team was born.
That background in data and technology, and some foundation in mathematics and other scientific disciplines, was a great springboard for my becoming a data scientist; but it wasn’t a journey I would categorize as smooth, full of (sometimes tedious) books and long training, sleepless nights and Eureka moments, failures and victories.
Speaking of the last two, one lesson I’ve learned: You must be prepared for your data-science project to not arrive at the solution you’re seeking—or arrive at any solution at all. Even if that happens, though, almost every project will produce some thought-provoking discoveries along the way.
Last but not least, if you’re considering whether to join the world of data science, be aware: The decision to specialize in a couple of distinct subject areas, versus becoming a generalist, is a matter of curiosity, interest, ambition, ability, or simply time. But whatever the breadth and depth, playing an active part in the value chain, transforming data into something useful (whether for the organization, science, or humankind) is the key reason for the data scientist’s existence.
Matthew Krump, Data Scientist
I started my career in finance and worked mainly in fixed income and structured finance. Even though my undergraduate degree focused on accounting and finance, I still felt like I was missing the technical tools to understand certain important concepts, so at some point I started thinking about going back to school. I wasn’t sure exactly what I wanted to study, so I began taking the prerequisites for an economics or finance Ph.D., which included lots of math and a decent amount of statistics.
I ended up really enjoying the math and statistics classes; neither of these were subjects that I’d previously considered for graduate school. Ultimately, I enrolled in a statistics master’s program. Once I graduated, I got a job in Chicago at NORC, working as a statistician on the National Immunization Survey. Right around the same time I discovered Kaggle and became very interested in machine learning. I also started attending some of local data-related meetups, of which there are many in Chicago.
One night at the Machine Learning meetup, I met our Chief Data Scientist Simon Hughes. He mentioned that there was a data scientist opening at Dice and suggested that I apply. That was around May 2014, so I’ve been at Dice almost a year now.
Part 2: Debating whether to work in academia, how an elementary-school computer lab led to a data-science career, and some advice about the field.