Senior Data Scientist
Tasks
- Build adaptive models for evolving fraud typologies
- Collaborate with ML engineers to productionize models
- Create training data sets and explore knowledge graphs for fraud detection
- Deploy real time low latency machine learning models
- Develop machine learning and statistical models for fraud detection and prevention
- Engineer and manage features for risk models
- Implement drift monitoring and model performance tracking
- Optimize model training using hyperparameter tuning and feature selection
Perks/Benefits
- Flexible work model
- Generous annual leave
- Health insurance
- Home office setup contribution
- Laptop ownership
- Learning and development budget
- Life and accident cover
- Mental wellbeing support
- Paid parental leave
- Sabbatical
- Snacks and meals
- Volunteering and development days
- Work from abroad
Skills/Tech-stack
AWS | Azure | DBT | Data Drift | Data drift monitoring | Drift monitoring | Feature Engineering | Feature Selection | GCP | Gradient boosting | Graph Databases | Hadoop | Hyperparameter Tuning | Keras | Knowledge graphs | Logistic Regression | MLOps | Machine Learning | Model Monitoring | Neural Networks | PyTorch | Python | Real Time | Real-time Inference | SQL | Scikit-learn | Spark | SparkML | Statmodels | TensorFlow
Education
N/A
Roles
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