Analytical Background Questions
The candidate's analytical skills and experience with data driven problem solving, including statistics, data analysis projects, tools and languages used, and examples of insights that influenced product or business decisions. This covers academic projects, internships, or professional analytics work and the end to end approach from hypothesis to measured result.
HardTechnical
0 practiced
You're building a generative text model for automated customer support responses. Define a comprehensive evaluation framework covering automated metrics (perplexity, BLEU/ROUGE), semantic or embedding-based metrics (BERTScore), task-focused metrics (resolution rate, time-to-resolution), human-evaluation protocol (rubrics, inter-rater agreement), safety/bias checks, and production guardrails. Explain trade-offs and prioritization for product decisions.
HardTechnical
0 practiced
Explain instrumental variables (IV) in causal inference: define what an instrument is, state the key assumptions (relevance and exclusion restriction), describe how IV estimation recovers causal effects when treatment is endogenous, and give a concrete product-analytics example (e.g., randomized encouragement or geographic rollout) where IV could be used.
HardTechnical
0 practiced
Implement a permutation_test_auc(y_true, y_scores_a, y_scores_b, n_permutations=10000) in Python that computes a two-sided p-value testing whether two classifiers have different AUCs using permutation testing. Assume both models are scored on the same test set. Show core logic (no scikit-learn permutation wrapper) and discuss runtime and ways to optimize.
MediumTechnical
0 practiced
Explain uplift (treatment-effect) modeling and how it differs from standard predictive modeling. Describe common use-cases in marketing or personalization, model architectures (two-model approach, T-learner, S-learner, X-learner), evaluation metrics (Qini, uplift curve), and deployment pitfalls to avoid.
HardSystem Design
0 practiced
Design a streaming anomaly detection pipeline to detect spikes and drops in telemetry at 100k events/sec with <5s detection latency. Describe ingestion, aggregation, stateful streaming processing (e.g., Flink, Spark Structured Streaming), choice of detection algorithms (EWMA, moving z-score, online changepoint), thresholding and false-positive control, alerting architecture, and scalability/reliability concerns.
Unlock Full Question Bank
Get access to hundreds of Analytical Background interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.