The posting
Role Overview The Data Scientist is embedded within the Data Science and AI Division (DSAD), working as part of the Fraud Analytics Models (FAM) team. The role will support the maintenance and expansion of FAM 2.0, which detects misuse of public training grant.
This includes addressing the methodology and threshold review backlog; building and operationalising the FAM 2.0 Technical Dashboard; and assessing how new schemes can be progressively brought within FAM 2.0's fraud-risk coverage.
The candidate is expected to contribute to reliable fraud-risk analytics, clear methodology and sustainable implementation and to build up in-house capability along the way.
Key Responsibilities: • Methodology and Threshold Review: Review and recalibrate FAM 2.0 model methodologies and thresholds for prioritised backlog items. This includes backtesting, impact assessment, analytical specifications, validation approaches, gap analyses and documentation. • Risk Model Enhancement: Translate business, policy and scheme requirements into changes to fraud-risk scenarios, indicators, features, thresholds and analytical logic. • Technical Dashboard Build: Build and enhance monitoring for model performance (precision, recall), data drift and concept drift, including alert logic, visualisations and deployment support. Design performance measures that account for delayed and incomplete investigation outcomes. • New Scheme Assessment: Assess new schemes' structures, eligibility rules, claims processes and known fraud risks. Map relevant patterns against the existing FAM 2.0 risk taxonomy and recommend how each scheme can be brought into coverage. • Governance and Stakeholder Engagement: Maintain clear methodology and change records. Communicate findings, trade-offs, dependencies and recommended actions to programme owners, FAM stakeholders, engineers and management. • Documentation and Knowledge Transfer: Produce reproducible code, analyses and documentation throughout the engagement.
Requirements & Qualifications Educational Qualification & Experience Education: Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline. Experience: 3–5 years of hands-on experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics.
Technical & Professional Competencies • Strong hands-on proficiency in Python and SQL for data extraction, transformation, exploratory analysis, with experience building reproducible analytical workflows. • Hands-on experience developing, validating or enhancing predictive, risk-scoring, fraud-detection or anomaly-detection models, including feature development, back-testing, threshold calibration and performance evaluation using measures such as precision, recall and related trade-offs. • Experience translating business, policy or operational requirements into analytical specifications, model logic, indicators, rules or measurable risk hypotheses. • Experience working with complex, large-scale datasets, including assessing data quality, tracing data lineage, investigating anomalies and documenting analytical assumptions.
Preferred (Advantageous) • Experience handling the technical challenges to fraud or risk modelling, including imbalanced data and delayed, incomplete or evolving outcome labels. • Experience designing or implementing production model-monitoring approaches, including data drift, concept drift and alert thresholds. • Experience building analytical dashboards using Tableau or an equivalent platform. • Experience with CI/CD, MLOps controls or the operational deployment of analytical models. • Experience with Spark or PySpark and enterprise data platforms, particularly AWS Redshift and AWS Glue. • Experience with network or graph analytics to identify related entities or coordinated patterns. • Experience working with workforce, training, grants, claims or regulatory datasets. • Prior experience in Singapore public-sector engagements or familiarity with government data-classification requirements.
Soft Skills & Communication Attributes • Strong analytical and structured problem-solving skills, with a rigorous and detail-oriented approach to work. • Ability to structure ambiguous requirements into clear assessment, methodology and implementation phases. • Clear written and verbal communication, including the ability to explain technical findings and trade-offs to non-technical stakeholders. • Ability to integrate quickly into an existing team, working independently while collaborating across data science, engineering, platform, programme and business teams. • Proactive management of delivery risks, dependencies and competing BAU and enhancement priorities.
