Data Scientist - Contract

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

NEXBRIDGE RECRUITMENT PTE. LTD. seeks a data science professional in Singapore to recalibrate fraud analytics models and develop detection rules with robust validation.

You will build dashboards for monitoring performance and data drift, align schemes with risk frameworks, and translate business requirements into analytical indicators. The role involves working with large datasets, documenting methodologies and collaborating with stakeholders to implement model enhancements and communicate

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.

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.

Skills

Python

SQL

Data science

Fraud analytics

Model validation

Analytical reporting

Education

Bachelor's or higher in Data Science/Statistics/Mathematics/CS/Engineering/Economics

Tools

Tableau

Spark/PySpark

AWS Redshift

AWS Glue

Job description

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.

We regret that only shortlisted candidates will be notified.