Robin (Rob) Kras
Data Engineer — Databricks, Azure, and scalable data infrastructure
Leiden, Netherlands · robkraseu@gmail.com · LinkedIn · GitHub · Kaggle
Summary
Data Engineer working across Databricks, Azure, and the data pipeline from raw data to production. Focus on MLOps, infrastructure as code, and cloud-based data engineering.
Experience
Data EngineerCurrent
Rabobank
- Build and maintain scalable data pipelines and ML infrastructure in production environments.
- Specialize in MLOps practices, containerization, CI/CD automation, and cloud-based data engineering.
- Own application maintenance, production deployment via infrastructure as code, automated testing, and monitoring systems.
Research: Multimodal Sound Symbolism2024 – 2025
Leiden University
- Investigated cross-modal sound symbolism in vision-language models.
- Combined computational linguistics, computer vision, and cognitive science. Grade: 8/10.
- View on GitHub
Education
MSc Computer Science — Data Science & AI2024 – 2025
Leiden University
- Thesis: Cross-Modal Sound Symbolism in Vision-Language Models
- Grade: 8/10
BSc Computer Science — Minor: Data Science2020 – 2023
Vrije Universiteit Amsterdam
Technical Skills
| Category | Skills |
|---|---|
| Data & Analytics | Databricks, Apache Spark, SQL, Python, Pandas, Azure Data Factory, Azure Synapse |
| ML & AI | Machine Learning, MLflow, XGBoost, LightGBM, CatBoost, PyTorch, SHAP |
| Cloud & DevOps | Azure, AWS, GCP, Terraform, Docker, Kubernetes, CI/CD, Infrastructure as Code |
| Languages | Python, SQL, Scala, Bash, C/C++ |
Notable Projects
Rainfall Prediction
- Feature engineering, K-Folds cross-validation, and ensemble methods.
- Simpler algorithms (KNN) outperformed complex ensembles once properly optimized.
House Prices Prediction
- Regression techniques with domain knowledge and SHAP for feature importance.
Loan Payback Prediction
- Binary classification for financial risk assessment.
- Ensemble of XGBoost, LightGBM, and CatBoost with SHAP interpretability.
Music BPM Prediction
- Predicting song tempo from audio features, combining signal processing with ML.
Road Accident Risk
- Ensemble methods with temporal and weather interaction features; containerized with Docker.
Bank Marketing
- Customer response prediction with YAML-driven configuration for rapid prototyping.
Additional Projects
| Project | Key Techniques |
|---|---|
| Podcast Listening Time | Time Series, Stacking, Temporal Features |
| Optimal Fertilizer | Multi-label, MAP@3, Domain Knowledge |
| Titanic Survival | XGBoost, Feature Engineering |
| Credit Card Fraud | Imbalanced Data, SMOTE, K-Folds |
| Personality Type | SMOTE, Bayesian Optimization |
Open Source
ML Utilities Library
- Reusable machine learning components and helper functions for data science workflows.
- View on GitHub