Fraud Data Scientist
Tasks
- Apply financial fraud knowledge to anomaly detection
- Collaborate with cross functional teams to improve fraud models
- Communicate insights to non technical stakeholders through reporting
- Conduct A/B testing and validation
- Develop predictive models using decision trees random forests gradient boosted trees
- Perform data exploration preprocessing feature engineering modeling validation deployment
- Transform data streams into fraud risk signals
Perks/Benefits
- Employee assistance programme
- Family leave options
- Healthcare Scheme
- Hybrid work
- Pension scheme
- Volunteer days per year
Skills/Tech-stack
A/B | A/B Testing | Account takeover | Anomaly Detection | B testing | Boosted Trees | Card Fraud | Databricks | Decision Trees | Feature Engineering | Fraud Detection | Gradient Boosted Trees | Graph Databases | Machine Learning | Model Deployment | Model Validation | Mule Activity | Neo4j | Payment fraud | Python | Random Forests | SQL | TigerGraph
Education
N/A
Roles
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