Start by clarifying requirements and edge cases with the interviewer, then outline a pipeline: normalize case, remove punctuation, expand abbreviations, and strip corporate suffixes. Discuss trade-offs between rule-based and data-driven approaches, and mention scalability and maintainability.
Pro tip: Mention that normalization should be idempotent and consider using a configurable mapping for suffixes to easily update as new variations appear. Also, highlight the importance of testing with real-world messy data.
Ask about the expected input format, output canonical form, and whether the function should handle international names or only US banks. Confirm if there's a predefined list of canonical names to map to.
Outline steps: convert to lowercase, remove punctuation and extra spaces, expand common abbreviations (e.g., 'N.A.' to 'NA'), and strip corporate suffixes like 'Inc', 'Corp', 'LLC'.
Consider abbreviations with periods, ampersands, and special characters. Decide whether to remove all punctuation or replace with spaces. Handle multiple suffixes and order variations.
Write clean code with helper functions for each normalization step. Use regex for efficiency and consider precompiled patterns. Discuss time and space complexity.
Propose test cases covering capitalization, punctuation, abbreviations, and suffixes. Suggest using a mapping table for canonical forms and validating against a sample dataset.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data characteristics and business requirements: what fields are available for matching, what similarity metrics make sense, and what threshold defines 'plausible'. Then outline a fuzzy matching pipeline that normalizes names, computes similarity scores, and returns all external IDs above a threshold, discussing trade-offs between recall and precision.
Pro tip: Emphasize that returning multiple matches is often safer than forcing a single match in ambiguous cases, but you must also provide confidence scores and a way to handle downstream ambiguity (e.g., manual review or weighted voting).
Ask about the available fields (e.g., bank name, routing number, address), the acceptable false positive/negative rates, and how the mapping will be used downstream.
Select appropriate string similarity measures (e.g., Levenshtein, Jaro-Winkler, token-based) and normalize names (lowercase, remove punctuation, handle abbreviations).
Set a similarity threshold (or multiple thresholds for confidence tiers) and compute a composite score if using multiple fields.
For each internal ID, return all external IDs whose similarity score exceeds the threshold, along with the scores for transparency.
Explain how to evaluate precision/recall, handle performance at scale, and incorporate feedback loops for continuous improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.