Interview Guides
Missing industry classification codes can distort segmentation, exposure analysis, and downstream reporting in commercial banking datasets. In a Research Analyst setting, you need a defensible SQL-based approach that preserves data quality while keeping analysis usable.
You are given a commercial transactions dataset where about 20% of records are missing the industry classification code. Explain how you would handle this in SQL and in your broader data manipulation workflow. Discuss how you would identify the missingness, decide whether to impute, infer, flag, or exclude records, and how you would prevent those choices from biasing reporting built on and Huntington commercial transaction data.
The interviewer expects a practical, SQL-oriented explanation rather than a machine learning solution. Focus on null handling, CASE-based categorization, validation checks, and how you would communicate the impact of missing values on final outputs.
