Machine Learning & Statistical Modeling
This area is the bedrock of the Data Scientist role at Saint-Gobain. Interviewers want to ensure you have a "white-box" understanding of the models you use, rather than just treating them as black boxes. You will likely face questions about model selection, evaluation metrics, and the nuances of training models on industrial data, which is often imbalanced or contains significant outliers.
Be ready to go over:
- Supervised Learning – Deep knowledge of regression, decision trees, and ensemble methods like Random Forest or XGBoost.
- Model Evaluation – Choosing the right metrics (e.g., Precision-Recall vs. ROC-AUC) based on the specific business cost of false positives or negatives.
- Feature Engineering – Techniques for handling time-series data or high-cardinality categorical variables common in manufacturing logs.
- Advanced concepts – Deep Learning architectures, Bayesian optimization for hyperparameter tuning, and dimensionality reduction techniques.
Example questions or scenarios:
- "How would you handle a dataset where 99% of the labels are the same class in a predictive maintenance context?"
- "Explain the difference between L1 and L2 regularization and when you would prefer one over the other."
- "Walk me through the mathematical intuition behind a Gradient Boosting Machine."
Case Study & Technical Presentation
The case study is often the centerpiece of the Saint-Gobain interview process. You are typically given a dataset and a business problem—often related to supply chain or production—and asked to provide a solution within a few days. This stage evaluates your end-to-end workflow, from data cleaning to final insights.
Be ready to go over:
- Exploratory Data Analysis (EDA) – Identifying patterns, correlations, and data quality issues.
- Coding Standards – Writing clean, modular, and reproducible Python or R code.
- Business Translation – Summarizing technical findings into a 15-minute presentation for a panel of experts and managers.
Example questions or scenarios:
- "Based on the provided dataset, which three factors most significantly impact production yield?"
- "How would you deploy the model you built into a production environment?"
- "If the business goal changed from accuracy to interpretability, how would your model choice change?"
Data Engineering & Coding Basics
Saint-Gobain places a high value on candidates who are "competent in both ML and Data Engineering." You are expected to be comfortable writing efficient SQL queries and managing data pipelines. This ensures that the models you build can actually be integrated into the company's existing data infrastructure.
Be ready to go over:
- SQL Proficiency – Complex joins, window functions, and query optimization.
- Python Scripting – Basic algorithms, data structures, and the use of libraries like Pandas and NumPy.
- Data Pipelines – Understanding how to automate the flow of data from a source system to a model.
Example questions or scenarios:
- "Write a SQL query to find the rolling average of energy consumption over a 7-day period for each factory."
- "How would you optimize a Python script that is running too slowly on a large dataset?"
