We are seeking an experienced Machine Learning Modeling Lead to drive the development and deployment of advanced machine learning solutions within the banking and digital lending sector.
This is a hands-on technical leadership role, focused on developing innovative models that support critical business functions across the retail banking customer lifecycle, including credit risk scoring, collections, underwriting, fraud detection and customer segmentation.
The successful candidate will bring extensive experience in banking analytics, a strong understanding of customer and bureau data, and a proven track record of delivering machine learning models into production. You will also provide technical guidance to junior data scientists and modelers, working closely with business stakeholders and cross-functional teams to turn complex business challenges into scalable, data-driven solutions.
Key Responsibilities
- Lead the end-to-end development of machine learning solutions, covering data exploration, feature engineering, model development, validation, deployment and ongoing monitoring.
- Design and deliver robust, innovative models that generate measurable business value across credit risk, collections, fraud and lending.
- Provide technical leadership, reviewing model submissions and supporting the development of junior data scientists and ML modelers.
- Oversee model documentation, technical reviews and formal model submissions.
- Manage and respond to queries raised by model validation teams, ensuring models meet relevant standards and requirements.
- Collaborate with implementation teams to deploy models into production environments, across both cloud and on-premises infrastructure.
- Work closely with business stakeholders to understand banking and financial services challenges and translate them into effective analytical solutions.
- Establish and promote best practices across model development, validation and deployment.
- Evaluate emerging technologies, tools and frameworks to drive continuous improvement in machine learning capabilities.
- Contribute to internal research and encourage a culture of innovation and experimentation.
Candidate Requirements
- 9+ years of experience in applied machine learning and model development within banking, financial services or FinTech.
- Strong understanding of retail banking analytics and the end-to-end customer lifecycle.
- Experience working with banking data, including credit bureau data.
- Demonstrable experience leading machine learning projects and providing technical direction to teams.
- Strong background in developing, deploying and monitoring machine learning models in production environments.
- Domain knowledge across collections, credit underwriting, credit risk and fraud detection.
- Understanding of ethical considerations and regulatory requirements associated with machine learning in financial services.
- Excellent problem-solving, communication and stakeholder management skills.
- Master's degree or equivalent in Computer Science, Data Science, Statistics, Applied Mathematics or another relevant quantitative discipline.
Technical Skills
- Advanced Python and SQL skills.
- Strong experience with machine learning libraries including NumPy, Pandas, Scikit-learn, TensorFlow and PyTorch.
- Expertise in statistical modelling and machine learning techniques, including:
- Regression and classification.
- Decision Trees and Ensemble Methods.
- XGBoost, Gradient Boosting Machines (GBM) and Random Forest.
- Unsupervised Learning.
- Deep Learning and optimisation techniques.
- Familiarity with MLOps frameworks and technologies such as MLflow, Kubeflow, Airflow, Docker and Kubernetes would be advantageous.
- Strong ability to communicate complex technical concepts and modelling approaches to both technical and non-technical stakeholders.
