The posting
Key Responsibilities
- 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.
Requirements & Qualifications
- 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 evaluation, including precision and recall.
- Ability to translate business or operational requirements into analytical specifications, model logic and measurable risk indicators.
- Experience working with large datasets, data quality assessment and analytical documentation.
- Strong analytical, problem-solving and communication skills, with the ability to explain technical findings to non-technical stakeholders.
Preferred Skills
- Experience with model monitoring, data drift, concept drift and delayed or incomplete outcome labels.
- Experience building dashboards using Tableau or similar visualisation tools.
- Familiarity with CI/CD, MLOps, Spark/PySpark, AWS Redshift or AWS Glue.
- Knowledge of network or graph analytics for identifying related entities and fraud patterns.
- Experience with claims, grants, workforce, training or regulatory datasets.
- Familiarity with Singapore public-sector projects and government data-classification requirements.
Interested candidates are invited to apply via the “Apply Now” button.
We regret that only shortlisted candidates will be notified.
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Nexbridge Recruitment Pte Ltd|26C3616
EA Personnel: Guan Jiayi|R22109847



