The data science world can seem abstract from the outside, but on the inside it’s full of fascinating work. Today I sat down with Christo Lute, a business data architect and project manager for the Enterprise Master Data Management initiative at Boeing, and Director of Advanced Analytics at Analytics Guild. Christo graduated from Seattle Pacific University with a BA in Philosophy and University College Dublin with a Master of Science in Research Methodology and Quantitative Methods. 

Hi, Christo—thanks for making time to chat! Let me ask first, why did you choose a career in data science?

Data science is simultaneously lucrative, philosophically compelling, and explicable to the everyday person. I wanted to make money, work on challenging and stimulating problems, and still be able to talk to my friends about my work.

Your background includes training in philosophy, which for the initiated might seem surprising—how does philosophy translate into your work?

Two direct ways. First, data science asks questions about what we can know, which is epistemology. Whether you're working on describing the nature of a data entity down to a logical level or justifying a hypothesis from a large sample population, you are making claims about what you able to know.

Second, there a tremendous number of data questions that tie into ethical dilemmas. Should we capture everyone's social media activity in order to prevent crimes? Should we use data to build better manufacturing machines, even if it displaces thousands of jobs? You can't talk about any data-centric field without bumping into a challenging moral quandary.

What do you wish non-initiated folks knew about data analytics?

So much of the data-science echo chamber is about mastering computer languages, high-level math skills, and keeping up with the dozens of new technological advances in the field in a given year. I think this all fine, but it can be overwhelming to the point of discouraging for folks new to the field.

Instead, I think the key is to understand the reasoning behind data, or a given technique or tech stack. It's far more important that you understand why you would want to do multiple-regression analysis than it is to have the formulas memorized to crunch the numbers.

My advice to newcomers: hone in on the problems you want to solve in your favorite industry, and then learn about the specific types of information that might help you address those problems. The techniques and tech will flow from there.

What has been the proudest moment of your career so far?

On a large scale, some of the most important work I've done in my career was while I was working as a statistical analyst for a medical consulting company right out of school. I initially started as just a grunt double-checking formulas and calculations, but after I noticed a few logic errors, I was able to show that a given lung cancer drug was more dangerous than it seemed at first blush. I think I might have literally used regression analysis to save lives.

Tell me what you think the most interesting issue facing the data industry is today.

I'm concerned that many of the technologies we're cooking up in data science will be used to power artificial intelligence. I'm not concerned about crazy Skynet scenarios as much as I'm concerned with what might happen if someone invents an A.I. with the capability of self-improving. In short order, that machine could become extraordinarily smart, so much so that if its inventor wanted to cure every disease, predict the stock market, and hack every government in the world, it would be a trivial task to do so. I don't trust human beings to use that kind of technology in a way that would benefit all of humanity and not just a select group.

There's clearly an ethical dimension to data science! How has your work meshed with your sense of doing good in the world?

Data science is changing the world for the better. It's making medical decisions safer, it's making automated-vehicles a reality so we have less car accidents, and it's making committing crimes more challenging. It's pushing the boundaries of what humans can do and know. While I'm confident that most of the work that I will do over my life will have virtually no impact on the outcome of human history, my mere participation in the data science space allows others to engage in that space as well. I'm a participant in pushing forward human knowledge, and together we're using data to crack some of the biggest puzzles and dangers human beings have encountered.

Want to read more from Christo Lute? Check out our blog on one of the most abused lessons in business: correlation vs. causation.

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