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Prep plan
Updated weekly · Last refresh Sep 25

Thomson Reuters Applied Scientist Interview Questions

The questions to prepare for a Thomson Reuters Applied Scientist interview. Questions from real interview reports rank first. Updated daily.

30questions
~4htotal time
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1
Model EvaluationStart here. 3 questions · ~24 min
Validate Model GeneralizationMedium

How to validate that a model will generalize to unseen data using holdouts, cross-validation, and calibration checks.

Hyperparameter TuningCross-ValidationBias-Variance TradeoffThomson Reuters
Choose Classification MetricsMedium

Choose the right classification metrics, and explain when precision, recall, and F1 score matter most.

F1 ScorePrecisionRecallThomson Reuters
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2
NLP4 questions · ~32 min
Explain Transformer Self-AttentionHard

Explain how transformer self-attention works, including its role in sequence modeling and why it scales better than RNNs.

Neural NetworksLanguage ModelsDeep LearningThomson Reuters
NLP Take-Home Task ApproachHard

Explain how to structure, implement, evaluate, and communicate an NLP take-home when written work matters more than live coding.

NLPMachine Learningsoftware engineeringThomson Reuters
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3
System Design6 questions · ~48 min
Design Feature Drift Monitoring SystemHard

Design a production ranking system with robust feature drift monitoring across batch and real-time features at high QPS.

Feature StoreFeature DriftModel ServingThomson Reuters
Design Thomson Reuters News FeedHard

Design an end-to-end personalized news feed for Thomson Reuters, including retrieval, ranking, serving, evaluation, and monitoring.

ML RankingFeature DriftdesignThomson Reuters
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4
Coding3 questions · ~24 min
Baseline Text Classifier ScriptMedium
Practice

Tokenize unstructured text, build bag-of-words features, and train a baseline Naive Bayes classifier.

CodingBasic AlgorithmspythonThomson Reuters
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5
Behavioral & Leadership12 questions · ~97 min
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6
More topics2 questions · ~16 min
Handle Highly Imbalanced ClassesMedium

Build a classifier for a highly imbalanced dataset and choose training and evaluation methods that surface rare positives.

Cross-ValidationFeature EngineeringSupervised LearningThomson Reuters
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