Data Scientist | Contract
- Anstellung
- Befristet / Vertrag
- Erfahrung
- ab 3 Jahren
- Methoden
- CI/CD, MLOps
- Ausbildung
- Hochschulabschluss
Review and recalibrate fraud analytics model methodologies and thresholds through back-testing, impact assessments, validation and gap analysis. Enhance fraud-risk models by translating business, policy and scheme requirements into risk indicators, features, detection rules and analytical logic. Develop and improve monitoring dashboards to track model performance, precision, recall, data drift and concept drift.
Assess new schemes, identify potential fraud risks and map them against existing risk detection frameworks. Analyse complex datasets, investigate anomalies and assess data quality to support reliable model development. Maintain clear documentation of methodologies, model changes, analytical findings and recommendations.
Collaborate with business stakeholders, data scientists and engineers to implement model enhancements and communicate findings.
Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related discipline. 3–5 years of relevant experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics. Strong hands-on proficiency in Python and SQL, including data extraction, transformation and exploratory analysis.
Experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models. Familiarity with feature engineering, back-testing, threshold calibration and model performance evalua