Katkat

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Mixpanel interview questions

7 practice questions in the style of Mixpanel's interviews, covering Data Manipulation (SQL/Python), Coding & Algorithms, Machine Learning.

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Mixpanel interview questions by the numbers

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Katkat has 7 Mixpanel interview questions. The largest topics are Data Manipulation (SQL/Python) (57%), Coding & Algorithms (29%) and Machine Learning (14%). By difficulty: 100% medium. The most common round is Technical Screen (100%). Frequent concepts include Data Aggregation, Hash Maps, Nested Structures and Recursion.

Topics

Difficulty

  • Medium100% · 7

Interview rounds

  • Technical Screen100% · 7

Roles

  • Data Scientist71% · 5
  • Software Engineer29% · 2

Frequent concepts

  • Data Aggregation14% · 1
  • Hash Maps14% · 1
  • Nested Structures14% · 1
  • Recursion14% · 1

Most mentioned in interview experiences

  1. 1.Calculate First-Week Retention by Signup Cohort1 report
  2. 2.Compare First-Week Retention by Plan Type1 report
  3. 3.Count Users Active After Signup1 report
  4. 4.Count Users Retained on Day Seven1 report
  5. 5.Validate Features That Predict User Retention1 report

Counted from 2 candidate interview experiences on Katkat. How we collect and count

Showing 1–7 of 7 results

  • Calculate First-Week Retention by Signup CohortMedium

    Compute first-week activity retention by signup-week cohort with all cohort users in the denominator and zero-retention cohorts included.

    MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
  • Compare First-Week Retention by Plan TypeMedium

    Compare first-week retention by recorded plan type with distinct-user counts, inclusive denominators, and four-decimal rates.

    MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
  • Count Users Active After SignupMedium

    Count distinct users with at least one event on or after signup using a PostgreSQL query.

    MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
  • Count Users Retained on Day SevenMedium

    Count users active on the seventh calendar day after signup, without double-counting repeated events.

    MixpanelData ScientistData Manipulation (SQL/Python)Technical Screen
  • Group Events by an Ordered Attribute ListMedium

    This problem tests recursive data structuring and hierarchical aggregation by mapping flat event logs to a nested tree of counts based on an ordered list of attributes.

    MixpanelSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement precise string-based decimal additionMedium

    This question evaluates a candidate's proficiency in string-based arbitrary-precision arithmetic, including decimal alignment, fractional carry propagation, normalization of numeric strings, and analysis of time and space complexity.

    MixpanelSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Validate Features That Predict User RetentionMedium

    Learn to evaluate retention features with leakage-safe time windows, cohort validation, baselines, ablations, and calibrated predictions.

    MixpanelData ScientistMachine LearningTechnical Screen

Mixpanel interview experiences

All 2 →
  • Four Retention SQL Questions in a 45-Minute ScreenMixpanel · Data Scientist · Technical Screen · Sep 2026
    In progress

    Straight to the useful stuff. My Mixpanel Data Scientist phone interview was a SQL screen, around 45 minutes, with someone from the DS team. Table structures, restated as I understood them, roughly like this: users: user_id, signup_date, plan_type, ... events: user_id, event_ts, ... There were four questions, each building on the previous one, all about retention analysis. Q1: How many users had…

  • Pandas Tasks and Open-Ended Retention PredictionMixpanel · Data Scientist · Technical Screen · Sep 2026
    In progress

    Following up on my previous SQL post: This was the Python screen in the same Mixpanel DS phone-interview round, about 45 minutes. There were four questions: two hands-on pandas tasks and two open-ended questions. Aggregate the users and events dataframes separately. Merge the two dataframes and check for duplicates. An easy one. Open-ended: Which features might predict retention? Explore the data…

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