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.

Skills

Machine Learning, Dashboards, Risk Assessment, AWS, Natural Language Processing, Amazon Redshift, Artificial Intelligence, Data Lineage, SQL, Python, Fraud Analytics, Data Science, AWS Glue, SQL Development, Data Extraction, Data Visualisation