Data Scientist

OPTIMUM SOLUTIONS (SINGAPORE) PTE LTD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

OPTIMUM SOLUTIONS (SINGAPORE) PTE LTD is seeking a data scientist to review and enhance risk-model methodologies, calibrate thresholds, perform back-testing and document analytical assumptions. The role emphasizes governance and clear communication with business stakeholders.

You will build dashboards to monitor model performance, evaluate precision and recall, and translate business requirements into analytical specifications.

Qualifications

• Degree in a quantitative discipline.
• 3+ years in data science, analytics, model validation or risk analytics.
• Proficiency in Python and SQL for data extraction, analysis and reproducible workflows.
• Hands-on experience building or validating predictive/risk models or anomaly detection.
• Experience with feature engineering, back-testing, threshold calibration and performance evaluation (precision/recall).
• Ability to translate business requirements into analytical specifications and model logic.
• Experience with large-scale datasets, data quality and data lineage, documentation.
• Experience developing dashboards or monitoring solutions to evaluate model performance.

Responsibilities

• Review and recalibrate model methodologies and thresholds, including back-testing and documentation.
• Translate business requirements into enhancements to risk scenarios and analytical logic.
• Build and enhance dashboards to monitor model performance (precision, recall, data drift, concept drift).
• Assess processes and operations for risk and recommend model-related improvements.
• Maintain methodology documentation and communicate findings to stakeholders and teams.
• Develop reproducible analytical code and documentation for knowledge transfer.

Skills

Python

SQL

Model development

Back-testing

Data analysis

Education

Degree in Data Science/Statistics/Math/CS/Engineering/Economics

Tools

SQL

Job description

Key Responsibilities

• Methodology and Threshold Review: Review and recalibrate existing model methodologies and thresholds, including back-testing, impact assessments, analytical specifications, validation approaches, gap analyses and documentation.
• Risk Model Enhancement: Translate business and operational requirements into enhancements to risk scenarios, indicators, features, thresholds and analytical logic.
• Technical Dashboard Development: Build and enhance dashboards to monitor model performance, including precision, recall, data drift and concept drift. Develop alerting logic, visualisations and performance measures that account for delayed or incomplete outcomes.
• Risk Assessment and Analytical Evaluation: Assess business processes, operational workflows and potential risks. Identify relevant patterns and recommend improvements to existing analytical models and detection frameworks.
• Governance and Stakeholder Engagement: Maintain methodology documentation and change records. Communicate analytical findings, trade-offs, dependencies and recommendations to business stakeholders, technical teams and management.
• Documentation and Knowledge Transfer: Develop reproducible analytical code, maintain technical documentation and provide knowledge transfer to support ongoing model enhancements and operations.

Requirements & Qualifications

• Education: Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline.
• Experience: Minimum 3 years of relevant hands-on experience in data science, advanced analytics, model validation, risk analytics or related analytical functions.
• Strong proficiency in Python and SQL for data extraction, transformation, exploratory analysis and reproducible analytical workflows.
• Hands-on experience developing, validating or enhancing predictive models, risk-scoring models or anomaly-detection models.
• Experience in feature engineering, model back-testing, threshold calibration and performance evaluation using metrics such as precision, recall and related trade-offs.
• Ability to translate business or operational requirements into analytical specifications, model logic, indicators, rules and measurable risk hypotheses.
• Experience working with complex, large-scale datasets, including data quality assessment, data lineage, anomaly investigation and documentation of analytical assumptions.
• Experience developing dashboards, analytical reports or monitoring solutions to evaluate model performance and support decision-making.
• Strong analytical, problem-solving, documentation and stakeholder communication skills.