Data Scientist, Fraud Risk Analytics (Public Sector Programme) | Contract

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Nexbridge Recruitment Pte. Ltd. is seeking a data scientist to recalibrate risk models, develop fraud indicators and monitor model performance. The role requires turning business requirements into analytical logic and maintaining reproducible analyses.

You will work across data science, engineering and business teams to ensure rigorous data handling and clear documentation. 3+ years in analytics and a quantitative degree are preferred.

Qualifications

• Degree in quantitative field such as Data Science, Statistics or Mathematics.
• Minimum 3 years hands-on data science, analytics or model validation.
• Proficiency in Python and SQL for data extraction and analysis.
• Experience building or validating predictive/fraud-detection models.
• Ability to translate requirements into analytical specifications and rules.
• Experience with large datasets, data quality and data lineage.

Responsibilities

• Review and recalibrate FAM 2.0 models, including back-testing and documentation.
• Translate requirements into fraud-risk indicators and logic.
• Build monitoring for model performance, data drift and concept drift.
• Design performance measures for delayed or incomplete outcomes.
• Assess schemes against risk taxonomy and document changes.
• Maintain methodology records and communicate findings to stakeholders.
• Produce reproducible code, analyses and documentation.

Skills

Model validation

Fraud analytics

Data analysis

Communication

CI/CD / MLOps

Data quality

Problem solving

Python

SQL

Exploratory analysis

Education

Quantitative degree (Data Science / Stats / Math / CS / Eng / Econ)

Tools

Python

SQL

Tableau

Spark/PySpark

AWS Redshift

AWS Glue

Job description

Job Responsibilities

• Review and recalibrate FAM 2.0 model methodologies and thresholds for prioritised backlog items, including back-testing, impact assessment, analytical specifications, validation approaches, gap analyses and documentation
• Translate business, policy and scheme requirements into changes to fraud-risk scenarios, indicators, features, thresholds and analytical logic
• 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
• 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
• Maintain clear methodology and change records
• Communicate findings, trade-offs, dependencies and recommended actions to programme owners, FAM stakeholders, engineers and management
• Produce reproducible code, analyses and documentation throughout the engagement

Job Requirements

• Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline
• 3 years of hands‑on 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 and 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 (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
• Strong analytical and structured problem‑solving skills, with a rigorous and detail‑oriented approach
• 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
• Able to integrate quickly into an existing team, working independently while collaborating across data science, engineering, platform, programme and business teams
• Proactive in managing delivery risks, dependencies and competing BAU and enhancement priorities
• Experience handling imbalanced data and delayed, incomplete or evolving outcome labels will be an advantage
• Experience designing or implementing production model‑monitoring approaches (data drift, concept drift, alert thresholds) will be an advantage
• Experience building dashboards in Tableau or an equivalent platform will be an advantage
• Experience with CI/CD, MLOps controls or operational deployment of analytical models will be an advantage
• Experience with Spark/PySpark and enterprise data platforms, particularly AWS Redshift and AWS Glue, will be an advantage
• Experience with network or graph analytics to identify related entities or coordinated patterns will be an advantage
• Experience with workforce, training, grants, claims or regulatory datasets will be an advantage
• Prior Singapore public‑sector engagement experience or familiarity with government data‑classification requirements will be an advantage

We regret that only shortlisted candidates will be notified.

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