Data Scientist

A prominent player in the financial services sector is expanding its credit modelling capabilities and seeking a highly capable Data Scientist to strengthen its analytics team. Renowned for its innovative use of data in driving strategic decision-making, this organization offers a dynamic environment where cutting-edge modelling and machine learning techniques are central to their approach. The team values collaboration, curiosity, and rapid delivery – and is looking for someone who thrives in high-velocity environments with tangible impact.

Role Responsibilities:

• Designing and deploying credit scorecards and behavioural models to predict repayment performance.
• Leveraging logistic regression and machine learning techniques such as XGBoost and Random Forests.
• Collaborating closely with data engineers to ensure model-ready, high-quality data pipelines.
• Ensuring rigorous testing, validation, and monitoring of models in production.
• Supporting and contributing to a robust MLOps framework for rapid model deployment.

Experience Requirements:

• 5+ years’ experience in data science, with a strong emphasis on credit modelling in Python.
• Proven track record in building, validating, and deploying models into production environments.
• Strong grasp of the credit risk lifecycle and relevant business impacts.
• Familiarity with ML tools and libraries (e.g. XGBoost, Scikit-learn, etc.).
• Ability to work effectively under tight timelines (30-day model delivery cycles are common).
• Preference will be given to candidates with experience in fast-paced, high-accountability teams.

Education Requirements:

• Bachelor’s or Master’s degree in a STEM field (e.g., Actuarial Science, Mathematics, Statistics, Engineering, or related).
• PhD candidates will be considered if they can demonstrate applied, hands‑on modelling experience.

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