Data Scientist Interview Questions

Showing 31–60 of 241 results

• Bank of AmericaMedium·Data Scientist
  Explain coding solution and alternatives

  Explain coding solution and alternatives evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsOnline Assessment
• B(Block (Square)Medium·Data Scientist
  Build referral chains and detect cycles

  This question evaluates graph algorithm competencies such as referral-chain traversal, cycle detection, handling noisy input data, and reasoning about time and space complexity for efficient per-node computation.

  Coding & AlgorithmsTechnical Screen
• Boston Consulting GroupMedium·Data Scientist
  Analyze Python Functions: Improve Readability and Efficiency

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Analyze Python Functions: Improve Readability and Efficiency states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• BByteDanceMedium·Data Scientist
  Compute Minimum Parentheses Additions

  This question evaluates proficiency in string processing and algorithmic reasoning for balancing parentheses, testing understanding of invariants and edge-case handling.

  Coding & AlgorithmsTechnical Screen
• BByteDanceMedium·Data Scientist
  Count Same-Day Registered Posters

  This question evaluates SQL querying skills, including joining relational tables, date handling, aggregation, and deduplicating users across events. Commonly asked in Coding & Algorithms interviews for data scientists, it assesses practical relational database querying and analytics tasks—filtering, grouping, and counting distinct users across tables—and tests practical application of SQL rather than purely conceptual database theory; this summary is in English.

  Coding & AlgorithmsTechnical Screen
• BByteDanceMedium·Data Scientist
  Determine Whether an Integer Is a Palindrome

  This problem tests your ability to manipulate integers mathematically to detect palindromic sequences without converting the number into a string, requiring careful handling of edge cases and integer overflow.

  Coding & AlgorithmsTechnical ScreenMathAlgorithmsInteger Manipulation
• BByteDanceEasy·Data Scientist
  Implement several OA simulation problems

  This set of problems evaluates skills in string processing, memory simulation and allocation logic, and online data-structure maintenance for dynamic contiguous segment counting, testing algorithmic design and attention to stateful invariants.

  Coding & AlgorithmsOnline Assessment
• BByteDanceHard·Data Scientist
  Implement stack variants and upward path sum

  A four-part ByteDance Data Scientist onsite coding question: implement an O(1) MinStack, a MaxStack with peekMax/popMax, a streaming median finder, and a binary-tree upward (node-to-ancestor) path-sum check. It tests data-structure augmentation, streaming order statistics, and prefix-sum tree reasoning.

  Coding & AlgorithmsOnsite
• BByteDanceMedium·Data Scientist
  Probability That One Fair Die Beats Another

  Solve a coding interview problem about the probability that one fair die beats another when the dice may have different sizes and duplicate face values. The challenge emphasizes strict tie handling and an efficient approach that avoids comparing every possible pair of faces.

  Coding & AlgorithmsTechnical Screen
• C3 AIMedium·Data Scientist
  Decompress an encoded string

  Decompress an encoded string evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsOnsite
• C3 AIHard·Data Scientist
  Find longest substring with at least k repeats

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find longest substring with at least k repeats states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• C3 AIHard·Data Scientist
  Find the Intersection of Two Linked Chains

  This problem tests the ability to detect the intersection point of two singly linked lists with constant space complexity, focusing on pointer manipulation and list traversal techniques.

  Coding & AlgorithmsOnsiteLinked ListsTwo PointersAlgorithms
• C3 AIMedium·Data Scientist
  Longest Substring Where Every Character Appears at Least K Times

  Find the longest substring in which every present character appears at least k times. Practice divide-and-conquer splitting, frequency reasoning, and fixed-alphabet complexity.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Scientist
  Automate Python Virtual Environment Setup on Linux Terminal

  This question evaluates proficiency in shell scripting and Linux terminal automation for Python virtual-environment setup, focusing on environment management, automation, and reproducibility skills relevant to data science workflows.

  Coding & AlgorithmsOnsite
• Capital OneMedium·Data Scientist
  Critique and Test Python Preprocessing Utilities Effectively

  This question evaluates a candidate's ability to review and critique Python preprocessing utilities, covering competencies in software design patterns (fit/transform separation), coding style and import hygiene, and unit-test specification for data-science pipelines.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Scientist
  Debug and test a Python function in venv

  This question evaluates understanding of Python runtime semantics (mutable default arguments, in-place list mutation during iteration), debugging and unit-testing practices, virtual environment and packaging workflows, and CI/tooling considerations.

  Coding & AlgorithmsOnsite
• Capital OneMedium·Data Scientist
  Explain Shell Script Line-by-Line for Data Science Workflows

  This question evaluates familiarity with shell scripting for reproducible data-science workflows, Python object-oriented design including transformer patterns (fit/transform separation), outlier detection and imputation strategies, code-style critique, and unit testing competency.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Scientist
  Explain Shell Script for Python Virtual Environment Setup

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain Shell Script for Python Virtual Environment Setup states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Scientist
  Fix failing tests and refactor code

  This question evaluates a candidate's ability to debug, test, and refactor Python code with attention to state management, mutable default arguments, deterministic unit testing, API design, and type-safety.

  Coding & AlgorithmsTechnical Screen
• Capital OneMedium·Data Scientist
  Optimize invites under capacity constraints

  This question evaluates a candidate's competency in probabilistic expected-value optimization, algorithmic efficiency for large-scale inputs, and constrained combinatorial selection including grouped all-or-nothing decisions and cost modeling.

  Coding & AlgorithmsOnsite
• Capital OneMedium·Data Scientist
  Refactor code and enforce robustness

  This question evaluates proficiency in code refactoring, robustness, input validation, unit testing, environment specification, and complexity analysis for Python data-processing workflows (pandas), within the Coding & Algorithms domain for Data Scientist roles.

  Coding & AlgorithmsOnsite
• Capital OneEasy·Data Scientist
  Review Preprocessing Code and Tests

  This problem tests your ability to identify common pitfalls in data science pipelines, specifically concerning data leakage, handling missing values, and software engineering best practices for reproducibility.

  Coding & AlgorithmsOnsiteData PreprocessingData LeakageCode Review
• Capital OneMedium·Data Scientist
  Validate Unit-Test Coverage and Identify Missing Scenarios

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Validate Unit-Test Coverage and Identify Missing Scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsOnsite
• CCargurusMedium·Data Scientist
  Reverse a Character Array In Place

  This problem tests the ability to manipulate array elements in-place using two-pointer logic while adhering to strict space complexity constraints.

  Coding & AlgorithmsTechnical ScreenArraysTwo PointersIn-place
• CitadelMedium·Data Scientist
  Implement Infinite Fibonacci Generator Using Lazy Evaluation

  This question evaluates understanding of lazy evaluation and Python generator semantics, testing a candidate's ability to work with iterative sequence production and manage state across iterations.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement Piecewise Linear Interpolation and Extrapolation

  Implement piecewise linear interpolation from unsorted knots, including exact-knot behavior and endpoint extrapolation. Practice paired sorting, binary search, and numerical edge cases.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement lazy unique-merge generator for sorted streams

  This question evaluates knowledge of lazy evaluation, Python generators/iterators, merging nondecreasing streams with duplicate elimination, and considerations of time and space complexity in streaming contexts.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Implement two-pointer unique-pair sum search

  Implement two-pointer unique-pair sum search evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Coding & AlgorithmsTechnical Screen
• CitadelMedium·Data Scientist
  Match a string with wildcard pattern recursively

  This problem tests your ability to implement a recursive pattern-matching algorithm that handles specific wildcard characters while managing overlapping subproblems.

  Coding & AlgorithmsTechnical ScreenRecursionDynamic ProgrammingString Matching
• CitadelHard·Data Scientist
  Sort a nearly sorted array

  This question evaluates algorithmic problem-solving skills, specifically understanding sorting under constrained inputs and rigorous time and space complexity analysis.

  Coding & AlgorithmsOnsite