Data Scientist Interview Questions

Showing 331–339 of 339 results

• WaymoMedium·Data Scientist
  Implement a Safe Average Function in Python

  Implement a Python average function that returns zero for an empty list and explain its time and space complexity. Clarify numeric types, missing values, NaN behavior, streaming alternatives, precision concerns, and the tests needed for a reliable data utility.

  Data Manipulation (SQL/Python)Online Assessment
• WaymoEasy·Data Scientist
  Merge overlapping intervals per group in pandas

  This problem tests your ability to handle temporal data by merging overlapping time intervals within distinct groups using pandas and custom logic.

  Data Manipulation (SQL/Python)OnsitePandasData ManipulationIntervals
• WaymoMedium·Data Scientist
  SQL: Average Ride Rating in Users' First and Third Active Months by City

  Write a PostgreSQL query that compares average ride ratings in each user's first and third active months, grouped by city and averaged over individual rides. Tests ranking distinct active months per user, window functions, NULL ratings and weighting.

  Data Manipulation (SQL/Python)Onsite
• WaymoMedium·Data Scientist
  SQL: Users With No Rides in the Most Recent 7-Day Window, Including Non-Riders

  Write a PostgreSQL query that lists users with no rides in the most recent 7-day window, including users who never rode. Tests anchoring a date window to the data, inclusive boundaries, and anti-join logic that keeps users without matching rides.

  Data Manipulation (SQL/Python)Onsite
• WeRideMedium·Data Scientist
  Compute window averages and merge intervals

  Compute window averages and merge intervals evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Data Manipulation (SQL/Python)Technical Screen
• Wells FargoMedium·Data Scientist
  Write SQL for rolling default rates

  This question evaluates proficiency with SQL window functions and time-series aggregation, specifically the ability to compute rolling metrics across customer segments by aggregating defaults and accounts over a moving 12-month window.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Calculate Monthly Customer Retention

  A PostgreSQL interview problem about calculating monthly customer retention from activity data. It tests month-level cohort construction, distinct-user counting, correct denominator selection, and handling customers who return across calendar boundaries.

  Data Manipulation (SQL/Python)Technical Screen
• WhatnotMedium·Data Scientist
  Compute a Seven-Day Rolling Average

  A PostgreSQL interview problem about computing a seven-day rolling average from dated observations. Candidates must use window frames carefully, preserve the requested grain, handle incomplete history, and return deterministically ordered results.

  Data Manipulation (SQL/Python)Technical Screen
• YahooMedium·Data Scientist
  Calculate Total Spend and Identify Key User Metrics

  This question evaluates a data scientist's proficiency in transactional data manipulation using SQL and Python, focusing on aggregation, ranking, joins, date arithmetic, and numeric rounding to produce user-level and time-series metrics.

  Data Manipulation (SQL/Python)Technical Screen