Two years ago, I was writing and debugging code line-by-line, spending hours on SQL queries, Python scripts, and documentation. Today, AI tools like Claude have fundamentally reshaped my daily workflow. While I still collaborate with teams and decide what's worth modeling, my approach has evolved dramatically.

Prompt engineering has become a core skill. My early prompts were lazy: 'Summarize the forecast accuracy.' Now I write detailed prompts specifying metrics, precision, and thresholds. This precision is crucial—I spend time testing prompt variants and double-checking outputs. The quality difference is enormous.

Cost management is critical with expensive LLMs. I use traditional ML when possible, clean data before feeding it to models, and route simple tasks to cheaper models. I cache repeated calls, track token usage, and apply feature selection to avoid overfitting.

Stakeholder communication has accelerated—I can draft slides in minutes instead of hours. But this means more frequent check-ins, not less work. The human skill remains vital: translating technical insights into actionable decisions. AI drafts the content, but I determine the point and ensure accuracy.