Data cleaning, studying data, looking for patterns, trying to figure out why the A/B test failed despite promising results offline. Finding / debugging annoying

Data cleaning, studying data, looking for patterns, trying to figure out why the A/B test failed despite promising results offline. Finding / debugging annoying bugs that caused that failure. Working with infra to operationalize some new way of doing inference in production. Wondering why certain features take so long to compute. Trying to scale up to dozens, hundreds, thousands of requests per second. Studying p99. Oh and occasionally you train a model on that data. But usually that's not the hard part. That's just the tip of a very very large pyramid with lots of underpinnings.