Breaking the Data Science Mold: Reflections after four BBQs in My First two Months in the US.

It’s been about two months since I landed in the U.S. from Europe, and instead of diving headfirst into the job market as I initially planned, I’ve been immersing myself in the freelance world (even though I had promised to take a few months before offering my services). I've had a few consulting meetings already, and it’s funny—I keep finding myself needing to explain: I approach data science a bit differently.

In one meeting, I was asked about the 'benchmark' I used for my machine learning models. It struck me that when someone focuses on the benchmark rather than the statistical metrics, the emphasis is more on the technological side rather than the statistical rigor or the economic interpretation of the results. This was a bit surprising, especially since the goal of developing complex algorithms was to analyze the behavior of certain economic agents. It’s fascinating—and a little frustrating—to see that many of the questions being asked are the same as those from ten years ago. No one asked about how I defined or applied complex concepts such as agent-based modeling, stochastic processes, network centrality, Nash equilibria, or Pareto efficiency, among others.

Because that’s what’s truly challenging when working with clients in sectors like finance, economics, and business—understanding and applying these deep, nuanced theories to real-world problems.

Don’t get me wrong, I’m all in for the hardcore stuff—statistics, AI, working in the cloud (AWS, Google, whatever you’ve got), or extracting insights from data providers like Snowflake, FactSet, or Refinitiv, among others. That’s all foundational, but here’s where it gets interesting: for me, it's not just about mastering an algorithm or mastering a cloud tool. It's about merging that technical expertise with the economic theories and strategies that have shaped markets for centuries.

The first half of my career was all about economic development consulting. I was knee-deep in analyzing regulations, designing institutional frameworks, and understanding incentive structures—basically, the building blocks of economies. Recently, I’ve had the chance to revisit this space, focusing on a specific Latin American country, applying quantitative methods to decode its economic dynamics. It’s been a refreshing reminder that while the tools have evolved, the core questions remain intriguingly the same.

Takeaway: statistical metrics vs. benchmark = statistical background vs. technological background.

In the second part of my professional career (since 2015), this is where my journey gets even more data-fun. I’ve been leveraging complex algorithms and AI to explore everything from pricing strategies and revenue models to network theory, game theory, and the chaos that is financial markets. I’m passionate about breaking down these sophisticated concepts because I believe that the intersection of economics, management, and data science is where real value is created. It’s not just about predicting stock prices or tweaking revenue models; it's about understanding the underlying mechanics that drive these outcomes. You can see many of the challenging concepts and innovative approaches in #Porandu, on my Medium page.

In fact, I have had the opportunity to talk with friends from law firms who need to understand how algorithms really work, how data is accessed, or if the data is synthetic or GDPR-compliant, or AI regulation compliant. The same thing happens with economic boutique firms—they use only macro and some sectorial data, but they need a big picture mixed with indicators that come from Wall Street, VIX, Russell 2000, or S&P, to name a few, and used in an innovative way. Not to mention companies in general that need to understand how to improve their share value, lowering costs and being more productive, or increasing their revenue with sustainable competitive strategies over time.

Explaining my green card status is like entering a whole different world—it's often quite challenging. I'm here on an EB1A visa, also known as the 'Einstein visa,' which is completely different from the EB1 WIN visa. But that's a topic for another post!

I'm curious if others see the value in bridging the gap between hard data skills and the nuanced art of economic theory. In my work, I’ve used algorithms and AI not just to crunch numbers but to provide insights into complex systems like pricing strategies, market dynamics, and risk management. This approach has proven successful in projects where understanding the deeper economic principles led to better decision-making and tangible business outcomes.

If you’re someone who values this unique combination, I’d love to chat more, perhaps over a coffee—or even better, at the next BBQ. Looking forward to new conversations, connections, and opportunities to explore how we can create value together!