This is the next interview in a new series in which we ask our unique and diverse Datasource Consulting staff to share a bit about themselves, both professionally and personally. We're amazed and inspired at their talents, hobbies, and aspirations, and we hope you are, too!
Years with Datasource: 1
Current Home City: Littleton, CO
Tell us about your experience with Datasource Consulting (DSC), including types of projects and positions held:
I started out at Datasource on a BI Assessment for a credit union. From there, I wrote a Tableau training manual for another credit union, and currently I’m serving as a Business Analyst at a healthcare company here in Denver.
Describe life at Datasource with three unique words.
Dynamic, Friendly, Earnest
Tell us what you like most about working at Datasource.
Undoubtedly the people and our collective push toward excellence.
What challenges you the most and what motivates you the most at Datasource?
Datasource is ripe with opportunity for someone in the data space – nobody is an expert at everything but DSC is chock full of experts and areas of opportunity for growth. I’m challenging myself this year to expand my knowledge in cloud computing and machine learning (specifically neural networks).
What is on your bucket list?
I would love to watch, or even race in, the Isle of Man TT (look it up – it’s insane).
What are your proudest accomplishments?
I graduated early, cum laude from the Colorado School of Mines and paid for my education by working as an econometrics intern.
What are your hobbies outside of work?
Fitness, reading, and motorcycle road racing (yes, that's really me).
What is your hidden talent?
I can do at least half of a back flip.
What phobias do you have?
Parasites. Which seems extremely reasonable *shudders*.
Tell us why you fell in love with data.
I find the scientific method and mathematics deeply philosophically appealing and data is the backbone of the scientific enterprise. It’s also interesting that our intuition about what “makes sense” is often empirically proven wrong when we make observations and scrutinize data. Data are like the breadcrumbs to truth.