Great modeling isn’t just technical. It’s behavioral.

We often emphasize technical fluency and process rigor in data science—knowing your models, tuning parameters, following best practices. But creativity? That part’s undervalued.

And yet, creativity is what separates a good modeler from a great one.

Data science isn’t just about fitting mathematical functions to data. It’s about replicating human behavior—using data alone. If we want to predict whether someone engages with an email, makes a purchase, or returns to a site, we need features that mirror the actual experience leading up to that outcome.

That’s not something you get from generic variables like simple counts or timestamps. It comes from engineered features that embody the context and nuance of behavior.

Strong feature engineering depends on two things:

• A deep understanding of the events surrounding the outcome
  
• The operationalization of those events into structured data

When features are too broad, they lack signal. Features that capture nuance—like repeated, unproductive visits or behavioral signals of friction—are what give models their depth and realism. They reflect the true texture of behavior.

And that’s where creativity thrives.

One of the most effective tools in this space? Empathy-driven design.

Put yourself in your user’s shoes. Go through the experience just like they would. Ask:

• What’s confusing?
  
• Where are the friction points?
  
• What assumptions might not hold in practice?

Never assume that a process works the way it’s designed. Never assume that intentionality becomes actuality. Never assume that what held true in one domain will work in another.

This hands-on, observational approach is where you start to see the opportunities for meaningful features—variables that capture hesitation, frustration, discovery, intent.

These aren’t just metrics. They’re embodied experiences, translated into data.

And when we approach modeling with this mindset, we do more than just predict outcomes. We build models that make sense—because they reflect the lived experience of the people behind the data.

That’s the creative edge we bring as humans in the loop.