Data Scientist

Job Description Education: Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics or a related quantitative discipline. Experience: 3–5 years of hands-on experience in data science, advanced analytics, model validation, fraud/risk analytics or programme analytics. Technical & Professional Competencies Strong hands-on proficiency in Python and SQL for data extraction, transformation, 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 using measures such as 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.

Experience handling the technical challenges to fraud or risk modelling, including imbalanced data and delayed, incomplete or evolving outcome labels. Experience designing or implementing production model-monitoring approaches, including data drift, concept drift and alert thresholds. Experience building analytical dashboards using Tableau or an equivalent platform.

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