Matt Ritter graduated from Olin as a mechanical engineer, and now works as a data scientist at athenahealth in Watertown, MA. He spent some time with me early this fall to share some reflections of his time at and post-Olin.
What he learned at college, on and off campus…
Matt joined the class of 2009, but took a leave of absence to work on other projects. One particularly meaningful project he did during that time was the Solar Decathlon. Taking place every other year, college teams from around the country built solar houses on the national mall in Washington D.C, and competed for saving the most energy. On this team, Matt worked with students from MIT to build and model the house (pictured above). He fondly recalls this event as a “…0 mile per hour NASCAR race...” because it required high intensity control of engineering systems, though without the roaring engines.
Matt also learned a lesson at The Solar Decathlon. By the end of the competition, it had started to rain. One of the older students was going to shut everything off to save power. Matt crunched the data and found that it would be best to shut off only a certain part of the house, and to keep the rest going. The team agreed to Matt’s plan, and his team finished two places ahead. From this, he learned that no matter who you are, if you bring the numbers, you have a place at the table.
As for the rest of his time at Olin, one of his favorite classes was nonlinear dynamics, taught by Professor John Geddes. Matt feels that this class changed his way of thinking about the world.
Life at athenahealth…
Olin played a big part in Matt’s job search as his graduation approached. athenahealth was at the career fair, and a friend of his had worked there and recommended it. After learning that it was a fun place to work where he could do what he loved, he thought it the ideal company and decided to apply. Having built a data science portfolio through various small projects, Matt was hired, and has worked there ever since.
Working in data science, Matt is on a small team deep within the company. On a typical day, he used to be pulling and interpreting data. Now, however, he and his team are making the process more efficient with a machine learning system that automatically finds and interprets information. Because he’s designing and developing machine-learning products, he considers himself an ‘internal entrepreneur’ within the athenahealth community.
And how did a mechanical engineering major lead to data science?
Before and during his time at Olin, Matt was drawn to clean technology, with a specific interest in solar concentrators. Because of the large part mechanical design plays in building solar power components, he chose mechanical engineering as his major.
Matt was then exposed to data science in a number of classes and projects at Olin. While he loved learning about mechanical theory in class, he soon realized that part of the process of mechanical engineering is looking at data, filtering it, and producing an answer. He really enjoyed that part of being a mechanical engineer.
Olin had no data-science specific degrees, so Matt chose to stick with Mechanical Engineering. He feels that this combination of experience and theory has resulted in a deliberate and strategic intersection of the fields.
Advice for Olin Students?
“Try thinking of your time at Olin as ‘product design,’ where your professional skills are the product. As you are learning in UOCD, the most important thing to do in product design is talk to your users. Look at what ‘products’ (in this case, other engineers) are already out there, and how people use those products (what their jobs are like).
Think about: How can you build your skills, so that they are valuable to your users (employers, graduate advisors, early startup customers)?
Then remember that your personal preferences are critical design constraints! What types of organization are employing your skill set? If you want to found your own startup, have you met many entrepreneurs with that skill set? Where do people like you go geographically, and into what industries? If you want to live in an expensive city, will you be paid enough to do it?
The one constant is that you need to collect data. From books, industry blogs, and at least a few real human beings.
I can guarantee you that some of your assumptions will be wrong (most of mine were!) and the only antidote is qualitative and quantitative user research.“