Data Scientist - Fraud & Risk Analytics (Public Sector)

At Xtremax, we're looking for a Data Scientist to strengthen fraud-risk analytics and improve the reliability of models used to detect misuse of public training subsidies. You'll work with a Data Science and AI team responsible for enhancing existing risk models, evaluating their performance and extending fraud-risk coverage to new schemes.

Your work will combine statistical analysis, model validation, feature development and practical implementation. You'll review model methodologies and thresholds, investigate performance trade-offs, and develop monitoring that accounts for real-world challenges such as imbalanced datasets and delayed or incomplete investigation outcomes.

You'll collaborate with engineers, analysts and programme stakeholders to turn business and policy requirements into measurable risk indicators and defensible analytical decisions. The role offers an opportunity to deepen your experience in applied data science while contributing to maintainable analytics and stronger in-house technical capability.

• Review and recalibrate fraud-risk model methodologies and thresholds through back-testing, impact assessments and validation.
• Enhance fraud-risk scenarios, indicators, features and analytical logic based on business, policy and scheme requirements.
• Evaluate model performance using precision, recall and other appropriate measures, balancing detection effectiveness with operational impact.
• Build and improve technical dashboards for model performance, data drift, concept drift and alerting.
• Develop performance measures that account for delayed, incomplete and evolving investigation outcomes.
• Assess new schemes by analysing eligibility rules, claims processes, scheme structures and potential fraud patterns.
• Map emerging risks against existing fraud-risk taxonomies and recommend how new schemes can be incorporated into model coverage.
• Analyse complex datasets, investigate anomalies, assess data quality and document assumptions and data lineage.
• Communicate analytical findings, limitations, trade-offs and recommended actions to technical and non-technical stakeholders.
• Maintain reproducible analytical code, methodology records and technical documentation, and transfer knowledge to the internal team.

• Degree or diploma in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline.
• Typically 3–5 years of experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics.
• Strong hands-on proficiency in Python and SQL for data extraction, transformation, analysis and reproducible workflows.
• Experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models.
• Understanding of feature development, back-testing, threshold calibration and model performance evaluation.
• Ability to translate business, policy or operational requirements into analytical specifications, risk indicators and measurable hypotheses.
• Experience working with complex datasets, including data-quality assessment, anomaly investigation and documentation of analytical assumptions.
• Strong analytical reasoning, attention to detail and structured problem-solving skills.
• Clear written and verbal communication, including the ability to explain analytical results and trade-offs to non-technical stakeholders.

• Experience working with imbalanced data and delayed, incomplete or evolving outcome labels.
• Experience with model monitoring, data drift, concept drift and alert thresholds.
• Experience developing dashboards in Tableau or equivalent platforms.
• Familiarity with CI/CD, MLOps or operational deployment of analytical models.
• Experience with Spark, PySpark, AWS Redshift or AWS Glue.
• Knowledge of network or graph analytics for identifying related entities and coordinated patterns.
• Experience with workforce, training, grants, claims or regulatory datasets.
• Familiarity with Singapore public-sector data governance and classification requirements.

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