Oscar Health·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

Jun 2026

Summary

Onsite coding round at Oscar Health for a software engineer role. One problem, pretty domain-specific for a health insurance company, involved geospatial filtering and set coverage logic together.

Questions Asked (1)

Given a list of healthcare providers (each with an ID, specialty, and location) and a list of members (each with an ID, location, and a set of required specialties), plus a max distance value, return the IDs of members who cannot find all their required specialties from providers within that distance.

Algorithms & Data StructuresAPI & Integrations

Author's notes

Two things happening at once here: distance filtering and then checking specialty coverage.

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AI HintsAI Generated

Suggested Approach

Clarify the distance metric (e.g., Euclidean or Haversine) and whether providers can serve multiple members. Then, for each member, check if every required specialty has at least one provider within the max distance; if any specialty is missing, include the member's ID in the result. Optimize by precomputing provider locations by specialty or using spatial indexing if the dataset is large.

Pro tip: Mention that in a real healthcare system, you'd likely need to handle edge cases like providers with multiple specialties, members with no requirements, and distance calculations using geospatial libraries (e.g., Haversine for Earth distances). Also, discuss trade-offs between brute-force and spatial indexing (e.g., k-d tree) for scalability.

1. Clarify requirements and assumptions

Ask about the distance metric (e.g., Euclidean vs. Haversine), whether providers can serve multiple members, and if specialties are case-sensitive. Confirm input/output formats.

2. Choose data structures for efficient lookup

Group providers by specialty and location, or build a spatial index (e.g., k-d tree, grid) to quickly find providers within a given radius for each member.

3. Iterate over members and check specialty coverage

For each member, for each required specialty, query the spatial index to see if any provider of that specialty is within max distance. If any specialty is missing, add the member's ID to the result.

4. Handle edge cases and optimize

Consider members with no required specialties (always covered), providers with multiple specialties, and large datasets. Discuss time/space complexity and potential optimizations.

5. Test and validate

Walk through a small example, test boundary conditions (e.g., exactly at max distance), and verify the output format.

Key Points to Mention

Distance calculation method (Euclidean vs. Haversine) and its impact on performance and accuracy.
Spatial indexing techniques (e.g., k-d tree, quadtree, grid) to avoid O(M*P) brute-force checks.
Grouping providers by specialty to reduce search space.
Handling members with empty required specialties (they are always covered).
Time and space complexity analysis, and trade-offs between precomputation and on-the-fly queries.
Edge cases: providers exactly at max distance, duplicate providers, and members with no nearby providers.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.