Data Scientist

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.