Field Notes

The question behind the question — short takes on what good data scientists ask before jumping to the model.

Field Notes is the short-form series in my Inference & Intelligence Lab.

These are my reflections from the day-to-day of working as a senior data scientist — the small judgment calls, recurring failure patterns, and “wait, why does everyone default to X?” moments that don’t always need a full essay.

What you’ll find here

⚖️ Tradeoffs that get skipped — cost, latency, quality, complexity.
🩹 Patches mistaken for fixes — when ML is optimizing around the real problem instead of solving it.
🧭 The diagnostic step — what to ask before reaching for a model.
🔬 Causal reasoning under pressure — when better models make the bar higher, not lower.