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How UVA Alumnus Nick Kalenichenko Is Bringing AI Solutions to Life
Nick Kalenichenko
Employment
Alyra Technology, Co-Founder and CTO (Charlotte, N.C.)
Education
M.S. in Data Science 2023, University of Virginia
B.A. in Statistics 2022, University of Virginia
For Nick Kalenichenko, the rapid rise of generative AI arrived at exactly the right moment. While pursuing UVA’s M.S. in Data Science, he built a foundation in natural language processing, data preparation, and responsible AI that helped launch his career at EY, where he developed early AI solutions for legal and compliance teams.
Today, as co-founder and CTO of Alyra Technology, Kalenichenko helps mid-size companies — particularly those in regulated industries — identify, build, and implement AI solutions that create real business value. He credits the MSDS program’s broad, practical approach, and UVA network with preparing him to move from technical problem-solving to leading client relationships, projects, and a growing team.
Q: What were you doing before the MSDS program, and how has the degree accelerated your career?
I was halfway through the MSDS program when ChatGPT was released, which turned out to be the best timing I could have asked for. My undergrad was in statistics at UVA, where I was also taking graduate-level CS classes and reading the academic ML papers behind these models to understand how they worked from first principles.
Then, I took Raf Alvarado's Natural Language Processing (NLP) elective course in the MSDS, which covered the fundamentals of transformer architecture, attention mechanisms, and embeddings. Which, you guessed it, is exactly what modern AI is built on.
Any data scientist will tell you that wrangling the data is 80% of the work, and architecting and testing models is the other 20%. I've found the exact same thing in industry. Even the largest companies deal with the same data problems smaller companies have, and often worse. The MSDS program drilled in both the importance of clean data and the practical steps to get there.
Those fundamentals opened the door at EY to work on some of the first AI projects inside our clients' legal and compliance departments, building proof-of-concept AI solutions for large enterprise clients. I sharpened my Python, learned to translate business requirements into working AI solutions, and, just as importantly, learned the compliance and legal landscape of getting AI adopted inside a regulated enterprise: what safeguards and guardrails need to be in place, and how to navigate those conversations with clients.
That experience propelled me to co-found Alyra Technology. We help mid-size companies, particularly in regulated industries, identify where AI can drive real value in their business workflows, and then our team designs and implements those solutions end to end. Alyra has since grown to a 12-person team.
Q: What does a typical day in your role look like, and what drew you to this position within the data science field?
No two days look alike. I have at least one coffee chat a day for business development. As Alyra has grown and scaled, I've moved off hands-on-keyboard work into more of a project leadership role, making sure client expectations carry all the way through the code, UI, and solutions we deliver.
Roughly, 20% of my time is business development, 60% is project management and client communication, and 20% goes to Alyra's brand and building our internal AI tools.
What drew me here is that I love talking to people, learning how businesses work, and spotting patterns across the companies I've worked with. I'm energized by making clients' ideas real and building long-term partnerships with them. My favorite part is the intersection I get to sit in now: understanding a client's work, telling them what's feasible and what the implications of putting AI in their business really are, and helping them figure out what will drive the most value as their next step.
Q: Can you share a specific project or problem at work where you directly applied skills from the MSDS program?
My capstone was with the U.S. Government Accountability Office. They needed to find reports across a repository of thousands, and all they had was keyword search. Working directly with their stakeholders, we built hybrid search, combining semantic and keyword search, directly into their system, so analysts could type out full paragraphs instead of one or two keywords and get the most relevant results. It saved them significant time and made their searches far more accurate.
And remember, this was before you could just ask an AI assistant how to build any of it. We figured it out with academic papers and Stack Overflow. That project turned out to be a preview of my job today: listen to stakeholders, understand the real problem, and ship a solution into their system that actually solves it.
Q: What made UVA's MSDS program stand out to you?
At first, I was a bit disappointed the program didn't go deeper into the technical weeds of specific machine learning models, which is where my passion was at the time. But I've come to appreciate the holistic approach. The program exposes you to a wide variety of areas within data science, and that's a great launchpad for the real world, where your job will inevitably touch several of them.
It's also a one-year program, and by the end you've seen, heard of, or worked with nearly everything the data science world can throw at you. You're not starting from scratch. You're starting from familiarity, which makes it much easier to dive deeper and stand out among coworkers and peers in the same positions.
Raf Alvarado's NLP course was the standout for me; the fundamentals it taught are the foundation of everything happening in AI right now.
Q: What's exciting to you within the industry right now?
I'm paying close attention to on-premises LLM solutions. For mid-size companies, especially in regulated industries where most of our clients at Alyra operate, running models on your own infrastructure is becoming increasingly attractive for cost, data privacy, and control. As open models keep improving, I think it's only a matter of time before more companies opt for on-prem deployments for their sensitive workflows.
I'm also watching the hardware side of that story. Demand for AI chips already outpaces supply, and a broader shift toward on-prem would only amplify it. How that supply picture plays out will shape the economics of the whole industry.
Q: What UVA experiences were most influential in shaping your career exploration and search process?
The alumni network, without question. Brilliant people graduate from UVA and go on to create and build their own businesses, and some UVA alums are now Alyra clients. The network helped me land my first job out of college, the UVA name goes a long way, and the professors in the program were top tier and pushed me to become the professional I am today.
Q: What advice would you give to prospective MSDS students?
I chose data science for its nimbleness. The field reinvents itself constantly, and there are so many directions you can take it. Data inside companies keeps growing and becoming more accessible, and companies will increasingly need people who can interpret it and facilitate how it moves between teams. That's what AI is great at: aggregating and distilling information so communication happens faster, across emails, messages, and meetings.
There's a classic economics idea called Jevons Paradox. As something becomes more efficient, demand for it grows. I see that as a pro for this field, not a red flag. Now is the most exciting time there has ever been to get into data science.
So my advice is to be optimistic and be a sponge. Learn as much as you can, take advantage of the professors and their industry backgrounds, and above all, use the network. The people you know will take you very far, so don't take anything the UVA network has to offer for granted.
Learn more about the full-time, in-person Residential MSDS at the University of Virginia. Request more information, connect with Admissions, or start your application today.
