Your question is Seasonality and Stationarity. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
For a Data Scientist (Time Series) role, how do you handle seasonality and stationarity in sequential data? Explain a practical workflow for identifying seasonal patterns, testing stationarity, applying transformations or differencing, and verifying that the resulting series is suitable for forecasting. State how you would choose a seasonal period and distinguish deterministic seasonality from changing variance or trend. Mention relevant diagnostics, model implications, and risks such as over-differencing and data leakage.