Hahnemann Ortiz: Evolution of a data scientist
Hahnemann Ortiz has a long history with the University of Minnesota’s Computer Science & Engineering department. In the late 1990’s he started taking classes via UNITE while working in software development at IBM in Rochester. A decade later, the challenge to design, develop, and maintain applications in different industries led him to the Master of Software Engineering (MSSE) program. Now, in his current position with the Internal Revenue Service, he identified an opportunity to introduce data science to replace critical business intelligence activities. This brought him back to the University once again, this time as a student in the M.S. in data science program.
What inspired your pivot to data science from software development?
I always had an interest in theoretical foundations, specifically, in algorithms, data structures, and programming languages. In recent years, the size, complexity, and variety of government data at work presented an opportunity to apply this knowledge. I became particularly interested in the mathematical and statistical foundations of machine learning algorithms, and the growth of machine learning and artificial intelligence for correct and efficient analysis of data. In short, I call the migration from software development to data science as “software engineering 2.0”.
Tell us about your work with the IRS.
I work for the Large Business & International Division, which is responsible for tax administration activities for businesses with a tax reporting requirement exceeding $10 million. I recently managed the data science implementation of the Large Case Corporate Compliance program using machine learning. Currently, I am developing a formal data science framework to standardize the use of data science for my division.
How are you managing your full time job, a graduate research assistant position, and your coursework in the virtual environment?
In pseudo-code: !easy && !impossible
You have to observe time constantly and prioritize. In management we learn to say “no” to many things, and this is necessary to accommodate learning. As any other graduate student, you have to make sacrifices. Finally, the logistics of working in a virtual environment was an important MSSE topic that I have been able to apply during the pandemic.
What are your plans after you complete your master’s degree in data science?
I would like to continue and earn a Ph.D. in computer science. I have been thinking about perhaps slowly transitioning from my career in federal government into the world of academia. I feel an obligation to pass my knowledge and experience to new generations of computer scientists.
What advice would you give to those interested in studying data science?
Don’t wait! If you are interested in the multitude of machine learning and artificial intelligence applications, you should start right away. Don’t forget that as engineers, we have a moral obligation with society and must observe ethical standards, especially with the application of machine learning and artificial intelligence that continues to affect all of us.
Anything else you’d like to share?
I want to thank so many people in the Computer Science & Engineering department for being professional, attentive, and an inspiration! In particular, I want to thank Dr. Mats Heimdahl for being my professor and mentor in software engineering, and Dr. Dan Boley for his current guidance in data science - it is an honor to be his research assistant.