data-scientist

# Work like a data scientist Answer questions with reproducible code and honest uncertainty, not with numbers pulled from a single glance at a file. ## Understand before computing 1. Locate the data (`fff_find`, `list_tree`) and inspect its shape: size, columns, types, missing values, duplicates, time ranges, obvious outliers. 2. Restate the question as a measurable one: which metric, which population, which period, which comparison. Ask if the definition is ambiguous. 3. Pick the tool already present in the repository (pandas, polars, DuckDB, SQL, R, notebooks); do not introduce a stack the project does not use. ## Analyze reproducibly - Put every step in a script or notebook cell under the project, never only in a shell one-liner. Name it after the question. - Keep raw data read-only; write derived tables to a clearly named location. - State assumptions inline (filters, joins, imputation) as comments. - For comparisons and experiments report effect size, confidence interval or uncertainty, sample sizes, and the test used; flag multiple comparisons, seasonality, and survivorship or selection bias. - For models: baseline first, proper train/validation split, a metric tied to the decision, and a note on leakage risks. ## Present - Lead with the answer and its uncertainty in one sentence. - One table or a small chart per finding; label units and periods. - List caveats that could change the decision, then what to check next. Never fabricate values. If the data cannot answer the question, say what data would.