Viktor Skantze

Viktor Skantze, Who are you?

Viktor Skantze is a Data Scientist and Machine Learning Engineer with a PhD in mathematical modelling and a postdoctoral background from Harvard Medical School. He specializes in transforming complex, dynamic systems into structured, production-ready AI solutions.
With deep expertise in Bayesian modelling, optimization, time-series analysis, and machine learning, Viktor designs end-to-end systems that move from raw data to operational decision support. His work spans healthcare, government agencies, manufacturing, and industrial applications—where he combines mathematical rigor with practical software engineering.
Rather than focusing on isolated models, Viktor builds scalable analytical systems: from stakeholder scoping and architecture design to deployment in cloud environments. His approach emphasizes clarity, robustness, and real-world impact—ensuring that advanced analytics translates into measurable value.

What made you join Devies Digital Core?

I'm not interested in chasing fads and trends; I'm interested in understanding what the client genuinely needs and delivering that.

I joined Devies Digital Core because they take a different approach to consulting – one where people are important and the work we do matters more than hours sold. Instead of the traditional body shop model, DDC focuses on the wholesome product we produce as teams and the value we provide. This approach gives us the freedom to do the best job we can without excessive limitations, and real influence over what we do and how we do it. 

Improvements to the work process happen organically, initiated by the people themselves. For me personally, this enables a greater sense of ownership over the solutions we build and creates space to experiment and refine both the product and the process. And to top it all off, the people are kind, which is a must.

What made you join Devies Digital Core?

I will work closely with clients to produce what I do best – wholesome software solutions that actually solve the problems at hand. I’m not interested in chasing fads and trends; I’m interested in understanding what the client genuinely needs and delivering that. 

Together with my colleagues, we can provide actual value by focusing on pragmatic solutions that work effortlessly for the people using them. By staying close to the problem and the people experiencing it, we can cut through unnecessary complexity and build software that matters.

Viktor Skantze

Viktor Skantze is a Data Scientist and Machine Learning Engineer with a PhD in mathematical modelling and a postdoctoral background from Harvard Medical School. He specializes in transforming complex, dynamic systems into structured, production-ready AI solutions.
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