Data Scientist III / IV

We are seeking a highly skilled and innovative Data Scientist to strengthen our credit risk modelling team. As a Senior Data Scientist, you will be a technical leader owning high-impact machine learning solutions across the entire borrower journey. You will partner with business leaders to drive growth, manage risk, and optimise operations.

Responsibilities:

• End-to-End Borrower Lifecycle Modelling: Develop and deploy advanced machine learning solutions across the entire lending value chain, optimising customer acquisition (propensity-to-borrow, uplift models, CAC/ROAS), strengthening credit risk underwriting (PD, LGD, risk-based pricing), and driving intelligent collections strategies (propensity-to-pay, dynamic recovery interventions).
• End-to-End Execution: Own the complete machine learning lifecycle, from translating ambiguous business requirements into mathematical frameworks to data pipeline creation, model training, and production deployment.
• Cross-Functional Leadership and Mentorship: Act as the strategic bridge between technical teams and business stakeholders (Risk, Growth, Ops). Mentor junior Data Scientists, conduct rigorous code reviews, and elevate the team's technical standards.

Requirements:

• We are looking for a seasoned practitioner with a deep understanding of algorithmic development and a proven track record of solving complex problems within the financial services ecosystem.
• Experience: 5-8+ years of hands-on industry experience in Data Science, with a core focus on FinTech, consumer lending, or banking.
• Domain Expertise: Deep understanding of lending economics and the regulatory landscape. Proven experience building models for at least two of the following: marketing/growth, credit risk scorecards, or debt recovery/collections.
• Programming Mastery: Expert-level proficiency in Python (Pandas, Scikit-learn, NumPy) and SQL for complex data manipulation and feature engineering on large-scale datasets.
• Machine Learning and Statistics: Deep practical knowledge of ensemble methods (e. g., XGBoost, LightGBM, Random Forests) and foundational statistical models (Logistic Regression, Generalised Linear Models).
• Familiarity with survival analysis, causal inference, and experimental design (A/B testing).
• Big Data and Cloud Infrastructure: Experience extracting and processing large datasets using distributed computing frameworks (e. g., PySpark).
• Proficiency within modern cloud ecosystems (AWS, GCP, or Azure).
• MLOps and Deployment: Solid track record of bringing models to production. Familiarity with version control (Git), orchestration tools (Airflow), and ML lifecycle management (MLflow, CI/CD pipelines, model monitoring).