Pinterest interview questions

Showing 151–161 of 161 results

• Solve five hard algorithm problemsMedium

  This problem tests your ability to optimize range-based operations using a difference array or prefix sum strategy and demonstrates your skill in balancing computational efficiency with memory constraints.

  PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
• Solve prefix, array, and string problemsMedium

  This problem tests your ability to efficiently manage prefix-based string searching using a Trie data structure, specifically focusing on handling wildcard character matching.

  PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
• Solve set equality and ad log top‑KMedium

  This multi-part question evaluates set and multiset operations, frequency counting, top-K computation over time windows, sliding-window and streaming data structures, and character-multiset matching for string construction within the coding & algorithms domain.

  PinterestSoftware EngineerCoding & AlgorithmsOnsite
• Support room moves and query top-k fastestHard

  This problem tests your ability to design a data structure that efficiently manages room assignments and retrieves the top-k rooms based on a primary priority (number of people) and a secondary priority (earliest arrival time).

  PinterestMachine Learning EngineerCoding & AlgorithmsOnsite
• Track a User's Top Trending Viewed Pin Within a Sliding Time WindowMedium

  A data structure design exercise: track the Pins a user has viewed within a sliding time window and, at any moment, return the one with the highest trending score. It asks for methods to add a viewed Pin, remove expired Pins and query the top Pin, plus the runtime complexity of each operation.

  PinterestSoftware EngineerSoftware Engineering FundamentalsOnsite
• Transform nested dicts with pandas apply/lambdaMedium

  This question evaluates proficiency in pandas-based data manipulation, including flattening nested list and dict structures, parsing nested attributes, computing per-user aggregates and segment metrics, and implementing robust, vectorized extraction logic that handles missing keys and avoids SettingWithCopy pitfalls.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Verify Machine-Learning Fundamentals for E-commerce Recommendation PlatformHard

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  PinterestData ScientistMachine LearningOnsite
• Write Queries for Pinterest Engagement TasksMedium

  This question evaluates SQL-based data manipulation and Python set/co-occurrence computation skills, covering aggregation, joins, timestamp-based cohorting and windowing, freshness detection, CTR calculation, and similarity scoring within a data scientist context.

  PinterestData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to compute max-overlap listsMedium

  This question evaluates proficiency in SQL data manipulation and set-based operations, focusing on deduplication, pairwise aggregation, and similarity metric computation (Jaccard) over relational tables.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Write SQL to rank categories by impressionsMedium

  This question evaluates proficiency in SQL data manipulation—covering joins, aggregations, grouping, tie-breaking, date-window logic, and scalability considerations such as indexing and query performance—in the Data Manipulation (SQL/Python) domain.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite
• Write windowed retention and ARPU SQLMedium

  This question evaluates proficiency with SQL window functions, joins, aggregations and time-windowed analytics for computing retention and ARPU, as well as handling date truncation, UTC day boundaries and common data edge cases across user, session, and order tables.

  PinterestData ScientistData Manipulation (SQL/Python)Onsite