Data Scientist Interview Questions

Showing 61–90 of 241 results

• CitadelMedium·Data Scientist
  Update No-Intercept Regression Slopes Across Batches

  Compute cumulative no-intercept regression slopes as paired data arrives in batches. Reuse running cross-products and sums of squares instead of refitting all historical rows.

  Coding & AlgorithmsTechnical Screen
• CoinbaseMedium·Data Scientist
  Detect sequence rule and repair anomaly

  This question evaluates a candidate's skill in sequence pattern recognition, algorithm design, complexity analysis, and robust integer arithmetic by requiring inference among arithmetic, alternating-difference, geometric, and Fibonacci-plus-constant models while tolerating at most one corrupted term under O(n) time and O(1) extra space constraints.

  Coding & AlgorithmsHR Screen
• CoinbaseMedium·Data Scientist
  Implement Plus One

  This question evaluates array manipulation and numeric carry propagation skills, along with the ability to reason about time and space complexity when performing arithmetic on digit lists.

  Coding & AlgorithmsOnsite
• CoinbaseMedium·Data Scientist
  Increment Digit Array

  This question tests the ability to handle array-based arithmetic and carry propagation logic, specifically addressing edge cases like array resizing.

  Coding & AlgorithmsOnsiteArraysMathSimulation
• CoinbaseMedium·Data Scientist
  Solve Aptitude Test: Logical, Numerical, Verbal Reasoning

  This question evaluates logical, numerical, and verbal reasoning along with rapid pattern recognition and time-limited decision-making relevant to pre-employment aptitude screens for data science roles.

  Coding & AlgorithmsOnline Assessment
• DRWMedium·Data Scientist
  Solve movie ratings, array, release scheduler

  This question tests algorithmic problem-solving across three domains: greedy array optimization, tree-based data structure design, and efficient sequence merging. It evaluates practical coding ability and complexity analysis, commonly used in quantitative and data science interviews to assess how candidates identify inefficiencies and select the right data structure to improve performance.

  Coding & AlgorithmsOnsite
• DRWMedium·Data Scientist
  Solve three algorithmic tasks in Python

  This question evaluates proficiency in dynamic programming, range-query data structures (Fenwick/segment tree), sorting and two-pointer techniques, and algorithmic complexity analysis within the Coding & Algorithms domain for Python-based implementation.

  Coding & AlgorithmsOnsite
• DiscordMedium·Data Scientist
  Count a Character in a String

  Count case-sensitive occurrences of a character in a string, including empty input and repeated matches.

  Coding & AlgorithmsTechnical Screen
• DiscordMedium·Data Scientist
  Return Words Present in Exactly One Message

  Find words appearing in exactly one of two messages using set symmetric difference while clarifying tokenization and case rules.

  Coding & AlgorithmsTechnical Screen
• DropboxMedium·Data Scientist
  Compute max island with constrained flips

  This question evaluates understanding of grid-based graph connectivity, connected-component labeling, and algorithmic complexity analysis (time and space) within the Coding & Algorithms domain, emphasizing practical algorithm design for large-scale inputs.

  Coding & AlgorithmsTechnical Screen
• EEtsyMedium·Data Scientist
  Implement streaming autocomplete under tight memory

  This question evaluates a candidate's ability to design a memory- and latency-constrained autocomplete service, testing skills in data structure selection, string/Unicode normalization, approximate string matching, concurrency, dynamic updates, and algorithmic time/space analysis.

  Coding & AlgorithmsTechnical Screen
• ExperianMedium·Data Scientist
  Design Algorithm for Longest Increasing Subsequence Length

  This question evaluates algorithm design and data structure skills, covering subsequence analysis, constraint/graph modeling for seating, and external-memory sorting considerations for scalability.

  Coding & AlgorithmsTechnical Screen
• Freddie MacMedium·Data Scientist
  Implement blocked time-series cross-validation

  This question evaluates competence in designing panel-aware time-series cross-validation with embargo handling, grouped blocking, deterministic splitting, scalability analysis, and adversarial unit-test design for issues like duplicate timestamps and entity migration.

  Coding & AlgorithmsOnsite
• GGeminiMedium·Data Scientist
  Detect k events in a sliding window

  This question evaluates a candidate's ability to design efficient sliding-window and streaming algorithms for time-series event data, encompassing deduplication, handling bounded out-of-order arrivals and clock skew, distinct-device counting, and meeting specified time and space complexity constraints.

  Coding & AlgorithmsOnsite
• Goldman SachsEasy·Data Scientist
  Count Palindromic Substrings

  This question tests your ability to identify palindromes within a string using expansion around centers or dynamic programming.

  Coding & AlgorithmsOnline AssessmentStringsTwo PointersDynamic Programming
• GoogleEasy·Data Scientist
  Build next-word predictor with O(1) lookup

  This problem tests your ability to design a memory-efficient data structure for storing n-gram probabilities and performing fast lookups for the most likely next word.

  Coding & AlgorithmsOnline AssessmentHash MapData StructuresNLP
• GoogleMedium·Data Scientist
  Compute precision–recall curve on imbalanced data

  This question evaluates a data scientist's competency in model evaluation for imbalanced binary classification, covering understanding of precision–recall dynamics, calibration, AUPRC, cost‑sensitive metrics, and practical implementation concerns like computing thresholds and handling ties and numerical edge cases; domain: Coding & Algorithms and machine learning model evaluation. It is commonly asked in technical interviews because it assesses both conceptual understanding of evaluation trade‑offs and the practical application of efficient, robust metric computation and threshold selection under asymmetric costs and operational constraints.

  Coding & AlgorithmsTechnical Screen
• GoogleEasy·Data Scientist
  Count super-streak segments in an event stream

  Count super-streak segments in a time-ordered event stream with an O(n) scan. The solution explains maximal segment boundaries, inclusive gap handling, duration and count checks, edge cases, streaming behavior, and changing-threshold follow-ups.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Implement Sampling and Minimize Loss in Numerical Coding

  This question evaluates proficiency in probability and numerical methods, focusing on sampling from truncated distributions and analytical properties of estimators under various loss functions.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Implement anagram check and stable deduplication

  This question evaluates streaming algorithm design, Unicode normalization and locale-aware text processing, multiset frequency counting under tight time and memory bounds for an anagram checker, and stable, order-preserving deduplication with custom keys and NaN handling; it is in the Coding & Algorithms domain for a Data Scientist position.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Implement longest subarray summing to k

  This question evaluates proficiency in array algorithms, prefix-sum reasoning, hash-based lookup patterns, and careful handling of edge cases and deterministic tie-breaking.

  Coding & AlgorithmsOnsite
• GoogleMedium·Data Scientist
  Implement percentage RMSE and bootstrap its CI

  This question evaluates implementation and statistical reasoning skills, focusing on robust numerical computation of percentage RMSE (including handling zeros/negatives, optional country weights, and numerical stability) and nonparametric bootstrap methods for confidence intervals.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Implement piecewise linear interpolation for time-to-empty

  This question evaluates numerical interpolation, time-series data cleaning, monotonicity preservation, extrapolation, and numerical stability skills applied to battery discharge curves, and falls under coding and algorithms within a data science domain.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Implement sampling and subarray scan

  This question evaluates probabilistic sampling and random-number transformation concepts for generating uniform points in a continuous domain, alongside array-processing and algorithmic analysis skills for identifying the longest strictly increasing contiguous subarray.

  Coding & AlgorithmsTechnical Screen
• GoogleEasy·Data Scientist
  Implement sampling, subarray scan, and percentile estimate

  This problem tests your ability to generate random uniform points within a geometric shape, process contiguous array properties using linear scans, and perform linear interpolation on histogram data to approximate percentiles.

  Coding & AlgorithmsTechnical ScreenGeometryAlgorithmsStatistics
• GoogleMedium·Data Scientist
  Match payments to invoices by memo or amount

  This problem tests string parsing, hash-map based data indexing, and multi-criteria reconciliation logic (fuzzy matching via IDs vs. exact matching via amount).

  Coding & AlgorithmsTechnical ScreenHashingStringsData Reconciliation
• GoogleMedium·Data Scientist
  Minimize L2, L1, and quantile losses

  This question evaluates understanding of statistical estimators and optimization principles for L2, L1, and tilted absolute (quantile) loss functions, including use of derivatives and subgradients, order-statistics, algorithmic complexity, and robustness considerations.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Normalize Columns in Binomial Matrix Efficiently

  This question evaluates proficiency in matrix manipulation, handling of binomial random samples, column normalization, and algorithmic efficiency for processing large numerical arrays.

  Coding & AlgorithmsTechnical Screen
• GoogleMedium·Data Scientist
  Simulate Coin Flips to Determine Fairness via Empirical Distribution

  This question evaluates a candidate's ability to perform simulation-based hypothesis testing by implementing Monte Carlo sampling and computing an empirical two-sided p-value from observed coin-flip counts, emphasizing statistical computation and numerical accuracy.

  Coding & AlgorithmsOnsite
• GoogleMedium·Data Scientist
  Simulate Uniform(0,1) from random bits

  This Coding & Algorithms question for a Data Scientist evaluates understanding of randomized algorithms, bit-level representation of real numbers, and probabilistic error and time-complexity analysis, situated at an algorithmic and mathematical abstraction level that requires both conceptual design and quantitative reasoning.

  Coding & AlgorithmsOnsite