March 2026
Julia Ekberg is a Stockholm-based risk data scientist at a regional analytics firm. We spoke about the realities of customizing volatility forecasting models for the Scandinavian context, the unique technical challenges, and the career impact of a strong peer feedback culture. Her experience highlights that local knowledge and honest critique matter as much as raw technical skill.
Most of my mornings are spent combing through local market data, looking for outliers that global models tend to ignore. Volatility forecasting in Scandinavia means adapting to regional quirks—seasonal shifts, regulatory events, and unique liquidity issues. My day is a cycle of testing hypotheses, discussing findings with peers, and refining models that rarely behave like their textbook versions.
The highlight is when collaboration uncovers a pattern none of us could spot alone. The challenge is convincing stakeholders that local context matters—what works for one region can mislead in another. The real reward comes from seeing a model actually improve after it’s been through a round of blunt peer review.
Global machine learning models often struggle with the nuances of local markets. We spend a lot of time retraining algorithms on regional data and running backtests against local shocks. The peer review process is where most mistakes are caught, especially those you’re too close to see yourself.
For anyone starting out: don’t underestimate the value of local data knowledge. Get hands-on experience with actual market feeds, not just sample sets. Listen to feedback—even the uncomfortable kind. That’s where your blind spots live.
Early in my career, I assumed technical skill would be the main barrier. In practice, the ability to communicate with peers and stakeholders—and to accept criticism—is just as important as coding ability. Technical growth depends on openness to feedback and a willingness to iterate.
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