Meet Nataliya, an AI consultant who combines academic background with practical industry experience. A principal data scientist with international experience and former lecturer in Machine Learning, Nataliya has led AI initiatives in the manufacturing, retail, and public sectors.
In this interview, she discusses how her background and real-world experience shape her approach to AI projects. We will shed light on AI’s opportunities and responsibilities and share practical thoughts on where AI is headed next.
Nataliya, thank you for joining us. Could you start by telling us a bit about your background and what initially led you into AI?
Of course! I’ve always enjoyed math and problem-solving. When I was studying math and computer science, I discovered machine learning and found it fascinating—it let me combine theory with practical problem-solving in all kinds of industries. After working on a few projects, I realized that data-driven approaches could really transform businesses, so I decided to focus on machine learning in both academia and industry.
You currently serve as a principal AI consultant. What does that role entail on a day-to-day basis?
It’s a blend of strategy and hands-on work. First, I help organizations figure out where AI can really make a difference, whether that’s optimizing supply chains or personalizing customer experiences. Then I lead data science projects—designing models, laying out data pipelines, and making sure everything is tested thoroughly. It’s not just about fancy algorithms; it’s about solving real problems and making sure the solutions last.
Speaking of technical solutions, which technologies do AI practitioners typically rely on, especially when building solutions for businesses?
Cloud platforms are usually a big go-to because they take care of many of the basics—storage, compute power, experiment tracking, etc. That means we can build and test prototypes faster, manage deployments more smoothly, and scale up when needed. They also have built-in monitoring and versioning, making tracking how the models evolve more straightforward. Of course, there are times when data privacy rules or very specialized needs mean we can’t just rely on the cloud, so we adapt to those cases.
You’re also recognized as a Google Cloud Champion Innovator. How does that tie into your approach to cloud-based AI solutions?
The recognition highlights a strong technical aptitude with Google Cloud products and a commitment to sharing knowledge with the community. It’s a wonderful validation of my work and a chance to stay connected with a vibrant community of cloud professionals. It also lets me collaborate directly with Google’s teams, keeping me at the forefront of new features and best practices, ultimately benefiting the clients I consult.
Generative AI has been quite a hot topic. Why do you believe it’s so transformative?
For me, generative AI stands out because of its accessibility and quick impact—almost anyone can try out a large language model and see immediate results. That tangibility makes the technology feel powerful and valuable. Beyond that, we’ve dramatically expanded the range of activities where generative AI can play a role. It’s no longer just about generating text; it can create images, write code, and more. The challenge is to use it responsibly and align it with real-world needs rather than just hype.
You mentioned your experience as a machine learning lecturer at Kharkiv National University. How did teaching shape your approach to AI in industry?
Teaching was incredibly valuable. It forced me to break down complex concepts into simpler terms, which really helps when explaining AI to clients or colleagues who don’t have a technical background. It also gave me a stronger appreciation for the foundational theory, which I think leads to better, more robust solutions in the long run.
How do you see AI making a lasting impact in education?
I’m really excited about AI’s potential to personalize learning and predict where students might need extra help. It can help keep learners engaged and on track. At the same time, we must be careful, especially with younger students, to ensure AI tools are used responsibly and don’t become distractions. Balancing innovation with accountability is key.
From your experience, what are the significant challenges in AI development, and how do you address them?
The first challenge is avoiding the “shiny object syndrome”—not every cool new AI technique actually solves a real business problem. You have to stay focused on clear objectives and measurable results. Another big one is navigating the legal and ethical side: making sure outputs are accurate, fair, and compliant. And of course, data can be a challenge—finding the right data, cleaning it, and ensuring it’s high-quality. To tackle these, I plan projects carefully, involve domain and legal experts, and test models thoroughly before rolling them out widely.
Finally, do you have any advice for aspiring AI professionals who want to follow a path similar to yours?
I’m a big advocate for diving deep into the technical details, but AI is such a broad field now that there’s no single path. Get hands-on experience—it’s a good way to learn. Pick an area that interests you, whether it’s computer vision or large language models, and start experimenting with real datasets. Focus on what excites you, learn the core theory, and build as many small, practical projects as you can. Don’t be afraid to fail a few times; that’s usually when you learn the most. Also, keep an eye on new frameworks and techniques—things change fast, and staying adaptable is huge.
Featured image credit: Matt Botsford/Unsplash
