This is the kind of question that sounds deceptively scoped until you realize you need to define what 'distance' even means before writing a single line of pseudocode.
Start by clarifying the business objective and what 'distance' means in this context, then propose a graph-based representation of the organization where nodes are employees and edges are weighted by various signals. Outline how to compute similarity using embeddings and graph algorithms, and discuss trade-offs between accuracy, scalability, and privacy.
Pro tip: Emphasize that the system should be designed for interpretability and privacy, as employee data is sensitive; suggest using techniques like federated learning or differential privacy to build trust.
Ask questions to understand the use case (e.g., team recommendation, expertise search) and define what constitutes distance/similarity (e.g., based on skills, interactions, hierarchy).
Identify data sources (org chart, collaboration tools, HR records) and extract features such as skills, projects, communication patterns, and reporting lines.
Select appropriate metrics (e.g., cosine similarity, Jaccard, graph distance) and algorithms (e.g., node embeddings, random walks) to compute pairwise scores efficiently.
Design a scalable pipeline for feature computation, embedding generation, and similarity search, considering batch vs. real-time processing and indexing structures.
Define evaluation metrics (e.g., precision@k, user feedback) and plan for monitoring, updating models, and handling privacy concerns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.