Optiver·Data Scientist·Onsite - System Design / Architecture·Intermediate

Intermediate

Apr 2026

Summary

Optiver Data Scientist interview with a system design question that felt more like a low-latency engineering problem than anything I'd associate with a DS role. Threw me off a bit but also kind of interesting once I got into it.

Questions Asked (1)

You're given a 7-8 character alphanumeric reference code and four candidate codes. Design a system that finds the exact match as fast as possible, handles streaming batches at increasing rates with low latency, and optionally tolerates OCR-style input errors via a configurable similarity threshold. Walk through your data structures, algorithmic choices, complexity, and how you'd benchmark and scale it.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a second to parse because it sounds deceptively simple.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (exact vs. fuzzy matching, latency targets, throughput, error tolerance) and then propose a hybrid architecture: an in-memory hash map for exact matches and a BK-tree or locality-sensitive hashing for fuzzy matches. Walk through data structures, algorithms, complexity, and a benchmarking plan that includes load testing and scaling strategies.

Pro tip: Emphasize that at Optiver, low latency and high throughput are critical, so you should discuss trade-offs between exact and fuzzy matching, and propose a tiered approach where exact matches are served first, with fuzzy matching as a fallback only when needed.

1. Clarify Requirements and Constraints

Ask about expected throughput, latency SLAs, error rates, and whether fuzzy matching is required. Confirm the character set and typical error patterns (e.g., OCR confusions like O/0, I/1).

2. Design Exact Matching Path

Propose an in-memory hash map (e.g., Python dict or C++ unordered_map) keyed by the reference code. Discuss O(1) average lookup, memory footprint, and handling of streaming batches via a concurrent queue.

3. Design Fuzzy Matching Path

For configurable similarity, suggest a BK-tree for edit distance or locality-sensitive hashing (LSH) for approximate nearest neighbors. Explain how to set the threshold and handle OCR-specific substitutions.

4. Analyze Complexity and Trade-offs

Compare time/space complexity of exact vs. fuzzy approaches. Discuss when to use each, and how to combine them (e.g., exact first, then fuzzy if no match).

5. Benchmark and Scale

Outline a benchmarking plan: measure latency percentiles (p50, p99), throughput, and memory. Discuss scaling via sharding, replication, and horizontal scaling with consistent hashing.

Key Points to Mention

Hash map for O(1) exact match with collision handling (e.g., separate chaining).
BK-tree for edit distance queries with O(log n) average search time for small thresholds.
Locality-sensitive hashing (LSH) for approximate matching with sublinear query time.
Configurable similarity threshold using Levenshtein or Jaro-Winkler distance.
Streaming architecture: use a message queue (e.g., Kafka) and parallel workers for low latency.
Benchmarking with tools like JMeter or locust, and monitoring with Prometheus/Grafana.

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