Senior Data Scientist
Tasks
- Build adaptive models for new fraud typologies
- Collaborate with ML engineers to productionize models
- Deploy real time low latency inference models
- Develop machine learning models for fraud detection
- Engineer and enrich features for risk models
- Explore knowledge graphs for relational fraud detection
- Handle imbalanced large datasets
- Implement model drift monitoring
- Monitor model performance and stability
- Optimize models to reduce risk
- Use SQL for data transformations and warehouse modeling
Perks/Benefits
- Generous annual leave
- Home office setup contribution
- Laptop ownership
- Learning and development budget
- Life and accident cover
- Mental wellbeing support
- OPD Coverage
- Paid parental leave
- Private health insurance
- Sabbatical
- Snacks and meals
- Volunteering and development days
- Work from abroad
Skills/Tech-stack
AWS | Azure | Continuous Deployment | Continuous integration | Continuous integration Continuous deployment | Cost-sensitive learning | DBT | Drift monitoring | Feature Engineering | GCP | Gradient boosting | Graph Databases | Hadoop | Hyperparameter Tuning | Keras | Knowledge graphs | Logistic Regression | MLOps | Machine Learning | Model versioning | Neural Networks | PyTorch | Python | SQL | Scikit-learn | Spark | SparkML | TensorFlow
Education
N/A
Roles
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