Data Science Lead (FinTech)

Key Responsibilities

Credit Risk & Modelling

• Develop and enhance credit risk models and acquisition scorecards to assess applicants.
• Build data-driven features and apply machine learning to improve model performance.
• Monitor model accuracy, stability, and compliance, and track portfolio health through dashboards and early warning indicators.

Data Analysis & Insights

• Analyze customer, transactional, and financial data to identify key risk drivers, trends, and anomalies.
• Support portfolio analytics such as vintage analysis, roll rates, and loss driver identification.
• Provide actionable insights to support data-driven decision-making across teams.

Credit Policy & Experimentation

• Design and evaluate credit strategies (e.g., cut-offs, limits, pricing).
• Run experiments and conduct post-implementation reviews to optimize portfolio performance.
• Support stress testing and scenario analysis.

Innovation & Automation

• Apply machine learning and advanced analytics to improve underwriting and risk monitoring processes.
• Explore new data sources and analytical approaches to enhance risk assessment.
• Collaborate with data and engineering teams to improve data quality and scalable pipelines.

Governance & Documentation

• Maintain clear documentation of models, features, and processes.
• Ensure compliance with internal standards and relevant regulatory requirements.

Key Requirements

Essential

• 3+ years of experience in data science, credit analytics, or credit risk within banking, fintech, lending, or consulting.
• Degree in Data Science, Statistics, Mathematics, or a related quantitative field.
• Strong background in statistical modelling, machine learning, and predictive analytics.
• Proficiency in Python and/or SQL.
• Experience developing and validating credit risk models, including scorecards and provisioning models.
• Solid understanding of model performance evaluation (e.g., ranking, calibration, stability) and business impact.
• Strong business acumen with the ability to communicate insights to both technical and non-technical audiences.
• Detail-oriented, proactive, and able to work in a fast-paced, iterative environment.
• Strong collaboration skills, with the ability to work across teams, markets, and time zones.

Desirable

• Experience in SME lending, merchant cash advances, or alternative credit products.
• Familiarity with IFRS 9, Basel, or similar regulatory frameworks.
• Experience working with bureau data, open banking, transactional or alternative data sources (e.g., behavioural signals).
• Exposure to cloud data platforms (e.g., Databricks, BigQuery, Snowflake, AWS, GCP, Azure) and version control tools (e.g., Git).
• Understanding of model monitoring, governance, and documentation in regulated environments.
• Knowledge of model interpretability techniques (e.g., SHAP, LIME).