Swish Analytics Data Scientist Interview Questions

The questions to prepare for a Swish Analytics Data Scientist interview. Questions from real interview reports rank first. Updated weekly.

Projecting With Limited Historical Data

Hard

Tests transfer learning, priors, and data-scarce modeling strategies for new leagues.

Feature EngineeringCold Startmodel training

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Decide whether a metric drop reflects a real shift or normal variation using hypothesis testing, confidence intervals, and baseline variability.

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

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SQL Window Functions for Rolling Stats

Medium

Tests SQL window function fluency for analytics features and ranking logic.

Window FunctionsRankingRunning Totals

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Designing Sports Betting A/B Tests

Hard

Tests experiment design, metric selection, and risk controls for Swish Analytics betting features.

experiment designGuardrail Metricsprimary metrics

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Core Product Metrics for Betting/Fantasy

Medium

Tests ability to define measurable KPIs aligned to product goals for betting and fantasy experiences.

MetricsKPIProduct Vision

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High-Frequency Tracking Data Pipeline

Hard

Tests data engineering design for streaming or near-real-time sports analytics pipelines.

Data QualityStream ProcessingETL

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Multi-Class Model Evaluation

Medium

Tests metric selection and evaluation strategy for multi-class sports classification.

Log LossClassificationF1 Score

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