Working with data to support better decisions.

Identity

I work as a data scientist on pricing, marketing, and customer-related decisions, helping translate data analysis into choices that can be used in real operations.

Before moving into industry, I spent almost two decades in academic research studying cellular mechanics and the actomyosin cytoskeleton. Much of that work already involved handling complex datasets and using computational tools, especially in microscopy and large experimental screens.

Over time I became more interested in programming and data analysis, and gradually worked toward transitioning into applied data science roles outside academia.

Today my work focuses less on models themselves and more on whether they help people make better decisions in practice.

What carried over from research

Research left a few habits that still shape how I approach problems:

• being comfortable working with uncertainty
• testing ideas instead of assuming they work
• separating signal from noise
• keeping solutions as simple as possible

Whether in research or in business contexts, the aim is usually the same: understand what is happening well enough to make better choices.

Current focus

I currently work on pricing and decision systems in financial services, with projects that typically involve:

• marketing targeting and customer experience analysis
• experimentation and performance measurement
• deploying and monitoring analytical models
• connecting analytical results with business decisions

Outside of work, I build small projects to explore ideas around learning tools and practical uses of AI, mostly as a way to keep learning and experimenting.

How I tend to work

I prefer pragmatic solutions over complex ones, and environments where people can question assumptions, discuss trade-offs openly, and iterate toward something that works well enough in practice.