Ana Bertran Ortiz is hard at work in the industry that the Harvard Business Review called “The Sexiest Job of the 21st Century.” She’s not quite sure what that means, but is happy to be working in her field regardless. She was recently featured in the Bloomberg article “Data Scientists Led by NASA Star Most Sought for Century: Jobs.” Ana may seem like she has “it all figured out,” but that goes against the very nature of data science. This fairly new industry is all about creating new molds and forging through uncharted territory.
A data scientist has to have a strong working knowledge of computer science as well as statistics, analytics, mathematics, and modeling. Ana’s background is no different. She has an impressive collection of degrees: Bachelor of Science, Master’s of Science, and a PhD in Electrical Engineering. She also has Master’s degrees in Industrial Engineering and Engineering Management, all from Stanford University. “You don’t get the opportunity to do the ‘fun’ stuff such as image processing until you have a solid background in math, science, and basic electrical engineering classes.”
Ana’s first year at Stanford was similar to most first years in college. She took different types of classes including math and science along with philosophy and psychology. It wasn’t until her junior year that she really started to move toward courses that directly related to electrical engineering. Ana found that she enjoyed courses on processing more than hardware classes.
Looking back, she recalls that the classes she enjoyed most were at the graduate level. “It can be hard to keep a good perspective of where you are going while you are taking the introductory classes. Some of the introductory classes, such as my case circuits, didn’t have much in common with what I actually do.”
What makes Ana a data scientist and not just a data analyst:
What does she actually do? What gives her the title of data scientist rather than data analyst? It’s her ability to look at large amounts of data from an open and inquisitive perspective.
IBM describes a data scientist as being “part analyst, part artist.” A data scientist will look at all incoming data rather than data from a single source. The goal in mind, as described by IBM, is to discover “a previously hidden insight.” In other words, she has the ability look at Big Data without going cross-eyed and come up with ways to turn what she discovers into new and helpful tools. A part of data science is being able to crunch numbers and figure out the statistics, but another large part is being able to work outside of boundaries, to go beyond the hypothetical models in order to find new and unique ways to achieve better results.
In order to be a data scientist, one has to be both mathematically and scientifically trained as well as empathetic and creative with a company’s needs.
Having co-founded AfterCollege, Ana was a perfect candidate for data science. She has the ability to be empathetic with the business side of things (having once been on that side) as well as the technical background to be able to analyze data and figure out ways to implement it.
Real world applications:
Ana’s father was a chemical engineer. She might have followed in his footsteps if she had not had a disappointing high school chemistry experience. Her high school chemistry course left her far too unprepared to further her studies in that field. I am sure that every business Ana has worked for is extremely thankful for her unpleasant chemistry experience.
Still, engineering as a whole could not be taken out of Ana’s future. It is in her blood. Not only was her father a chemical engineer, but her uncle was an electrical engineer. She remembers him discussing the concept of a “FasTrak” on a freeway with her at a young age. At the time, there was no such thing and everyone had to go through the slow toll booths. She remembers being impressed with the idea that this “FasTrak” could actually be built in the real world.
She chose to follow in her uncle’s footsteps and got her Bachelors, Master’s, and PhD in electrical engineering. While studying electrical engineering, Ana took a class on radar. The professor who taught the class was excellent and made everything very practical. He gave real-life examples of what you can do with radar technology. She enjoyed working with him so much that she ended up focusing her PhD in radar processing with him. This was perhaps the first sign that Ana was made for data science.
This “real world” application of radar processing is a very data science approach to teaching. In The New York Times article “Data Science: The Numbers of Our Lives,” data science is defined by its real world applications. The article states that data science is “spawned by the enormous amounts of data that modern technologies create.” With modern technology we are now able to collect data on just about anything. This creates the Big Data that data scientists are working with. We now have data on “the online behavior of Facebook users, tissue samples of cancer patients, purchasing habits of grocery shoppers, or crime statistics of cities.”
Recording data on everyday things, and using it to improve customer experience is a relatively new task. This means there are not many mathematical models to follow in order to organize and understand it.
That’s where data scientists come in. They look at this real world data, the enormous amounts of it, and come up with ways to understand and use it. One data science student is currently working to quantify the social sciences.
Ana has used her skills in a number of different positions. She has worked with JPL (NASA) as a radar processing research engineer. She designed algorithms that will be used in a satellite to measure sea levels. She was also able to use some of her radar skills to design flight paths based on desired properties of the output radar signal.
She spent some time working with her neighbor on Ourcast using radar and data analysis to create a short-term weather prediction mobile app.
She now works for Virtual Instruments, a tech company that is always looking to bring better technology to its clients. She loves the flexibility her current job allows. She is able to work both in the office as well as remotely.
The New York Times article describes the best data science students as “really curious people, thinkers who ask good questions and are O.K. dealing with unstructured situations and trying to find structure in them.”
That’s what Ana is currently doing as a data scientist for Virtual Instruments. Met with the challenge of “the new normal” described by Virtual Instruments as an expectation and “demand for anytime/anywhere application access and flawless performance—from any device,” she has been working on a software that will bring us closer to this ideal of flawless performance. It will diagnose network glitches automatically.
Ana’s job involves a lot of thinking before doing. There is no model to base her work on. She is constantly prototyping and modeling things to make everything more efficient. She loves the fact that she works in a relatively new field and that she has a lot of opportunity for innovation. She can come up with ways to apply data analysis and radar processing toward her company’s goals in ways that are totally new to everyone.
You would think that someone who has to be able to organize and keep track of large amounts of data every day would be somewhat O.C.D. in their life outside of work as well. When I asked Ana how her work reflected her everyday life, her answer was a surprising juxtaposition. Because of the large amount of thought that goes into her work, she is much less strategic about her daily life.
Yes, she likes to be prepared, and has a calendar to keep track of her children’s activities, but for the most part she likes to relax when she’s not at work.
Still, her skills have come in handy in some aspects of her normal day-to-day life. For example, she did research on the establishments before choosing which schools to send her children to. She has also used her skills while working at her daughter’s school with admissions and fundraising. Parents are asked to help out at the school, and with her background, she was able to make the process of selecting applicants more efficient.
“Our world is composed of lots and lots of data and by distinguishing the important data from the noise, one can get ahead of the game.” – Ana Bertran Ortiz
Homework time! Interested in becoming a data scientist? Read Steven Cherry’s interview with Chris Wiggins, an applied mathematics professor at Columbia University,“Is Data Science Your Next Career?” about where the study of data science is and where it is going. Also, check out Rachel Schutt’s blog about the course she taught at Columbia University called Introduction to Data Science. Also, for another look at how data science is working for a company, see Dan Woods’s interview “LinkedIn’s Monica Rogati On ‘What Is A Data Scientist?’” about being a data scientist for LinkedIn.