Data Scientist

Responsibilities:
Develop machine learning and statistical models for fraud prevention, risk scoring, and credit underwriting.
Analyse large-scale behavioural and transactional datasets to identify patterns, anomalies, and emerging fraud trends.
Design and run experiments to evaluate, compare, and improve model performance.
Partner with engineering to deploy models into production and ensure they are reliable, observable, and performant at scale.
Monitor live models for drift and degradation, and lead retraining and improvement cycles.
Collaborate with product and risk teams to translate business problems into modelling solutions.
Requirements:
Strong expertise in Python and SQL, and hands-on experience with ML frameworks such as scikit- learn, XGBoost, PyTorch, or TensorFlow.
Solid foundation in classical machine learning, statistical modelling, and experiment design.
Experience taking models from notebook to production, including monitoring and iteration.
Ability to communicate complex findings clearly to technical and non-technical stakeholders.

Experience
3-6 yrs