Hyundai Motor Data Scientist Interview Guide

Structure answers with Situation, Task, Action, Result. Emphasize the problem you solved (20%), the analytical approach and models used (40%), implementation details (20%), and quantified business impact (20%). Always include metrics and statistical rigor.

- **ML Model Selection**: Linear → Tree-based → Ensemble → Deep Learning progression - **Feature Engineering**: Domain knowledge → Transformation → Selection → Validation - **Model Evaluation**: Accuracy vs Precision/Recall vs F1 vs ROC-AUC based on problem - **Experimental Design**: Hypothesis → Sample Size → Test Design → Statistical Significance - **DS Workflow**: Problem Definition → EDA → Feature Engineering → Modeling → Evaluation → Deployment

**Phone Screen (45-60 min)**: ML fundamentals, statistics, SQL/Python coding basics **Technical Round 1 (60 min)**: ML algorithms deep-dive, model selection and evaluation **Technical Round 2 (60 min)**: Take-home case study or live coding with data analysis **Technical Round 3 (60 min)**: System design for ML, A/B testing, experimentation **Behavioral Round (45 min)**: Cross-functional collaboration, stakeholder communication

- Using complex models without justifying why simpler approaches won't work - Not discussing data quality, biases, or ethical considerations - Failing to communicate statistical concepts to non-technical stakeholders - Ignoring business context and focusing only on model accuracy - Not validating assumptions or testing for overfitting

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