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Shopify·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Got a Shopify data scientist interview that threw me a classic systems design problem dressed up as a coding question. Not what I expected for a DS role, but here we are.

Questions Asked (1)

Q1

Design and implement an LRU Cache class that supports get and put operations, both in O(1) average time. Evict the least recently used entry when capacity is exceeded.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew the answer involved a hashmap plus a doubly linked list but my first instinct was to just reach for an ordered dict and call it a day.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then explain the standard hash map + doubly linked list solution that achieves O(1) operations. Walk through the design, implement the key methods, and discuss trade-offs and potential optimizations.

Pro tip: Mention that Python's OrderedDict provides a built-in LRU cache, but interviewers often expect a from-scratch implementation to test your understanding. Also, relate LRU caching to real-world data science scenarios like caching model predictions or feature store lookups.

1. Clarify Requirements

Ask about expected capacity, concurrency needs, and whether the cache should be thread-safe. Confirm that get and put must both be O(1) average time.

2. Choose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains usage order for O(1) eviction and updates.

3. Design the Class

Define a Node class with key, value, prev, and next pointers. The LRU Cache class holds a dictionary, capacity, and dummy head/tail nodes for easy list manipulation.

4. Implement Operations

For get: if key exists, move node to front and return value; else return -1. For put: if key exists, update value and move to front; else add new node to front, and if over capacity, remove node before tail and delete from dict.

5. Analyze and Optimize

Discuss time and space complexity (O(1) time, O(capacity) space). Mention potential improvements like thread safety or using a built-in OrderedDict for simplicity.

Key Points to Mention

  • Hash map for O(1) key lookup and doubly linked list for O(1) order maintenance.
  • Use of dummy head and tail nodes to simplify edge cases (e.g., empty list, single node).
  • Eviction policy: remove the node just before the tail (least recently used).
  • Handling of get and put operations, including updating existing keys and moving nodes to the front.
  • Time and space complexity analysis: O(1) per operation, O(capacity) space.
  • Trade-offs: simplicity vs. performance, thread safety considerations, and alternative implementations (e.g., OrderedDict).

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