The Role

At Zempler Bank, data plays a critical role in how we design better products, manage risk responsibly and deliver great outcomes for our customers. As our Head of Data Science, you’ll lead and shape the bank’s data science function, setting the vision and roadmap for advanced analytics, modelling and machine learning across the organisation.

This is a highly influential role, sitting at the intersection of technology, risk and commercial decision-making. You’ll help us create and embed genuinely differentiated data driven capabilities that support our specialist banking proposition from credit decisioning and fraud management through to enhancing the end‑to‑end customer experience.

Hybrid Working

We are very proud to offer one of the most flexible hybrid working arrangements in the industry!

The expectation for this role, will involve a minimum of one day each week - working out of our London Bridge office.

Why join Zempler Bank?

Zempler Bank is a specialist bank with an ambitious growth agenda and a strong focus on doing the right thing for our customers. We value diverse perspectives, inclusive leadership and thoughtful innovation. You’ll have the opportunity to shape a function, influence the bank’s future direction, and do work that genuinely makes a difference.

Key Responsibilities

Strategic & Technical Leadership

• Define and deliver the bank’s Data Science roadmap, aligning capability with clear commercial value.
• Build and lead a high‑performing data science function with strong technical standards and delivery discipline.
• Act as a senior AI and Generative AI authority, enabling secure, responsible and practical adoption across the bank.
• Contribute to wider data strategy, maturity uplift and transformation initiatives.
• Champion transparency, scientific rigour and ethical modelling practices.

Model Development & Engineering Excellence

• Provide hands‑on leadership for statistical, machine learning and predictive model development.
• Ensure robust feature engineering, validation, testing, explainability and reproducibility.
• Oversee scalable modelling pipelines, reusable components and strong coding standards.
• Partner with Engineering to ensure models are effectively tested, deployed and operationalised using best‑practice CI/CD approaches.

Model Governance, Risk & Regulatory Compliance

• Apply the Model Risk Management Framework proportionately, ensuring appropriate governance, validation and monitoring.
• Represent Data Science at key risk and governance forums, supporting model approval, oversight and challenge.
• Ensure documentation, auditability and explainability meet PRA/FCA expectations.
• Oversee performance monitoring, drift detection and remediation, with enhanced controls for higher‑risk models.

Data Foundations & Quality

• Work with wider data teams to ensure models are supported by high‑quality, well‑governed data.
• Promote reusable features, datasets and clear documentation to enable scalable analytics.
• Advocate for improvements to data quality, definitions and controls where needed.

Stakeholder Engagement & People Leadership

• Build strong partnerships across Product, Risk, Compliance, Technology, Operations, Credit and Financial Crime.
• Represent Data Science across data, risk and technology governance forums.
• Lead, mentor and develop senior data scientists, building both technical depth and leadership capability.
• Foster a collaborative, high‑performance culture and manage external vendor relationships where appropriate.

Qualification, Skills and Experience

Essential

•  Experience in applied data science within a highly regulated or financial‑services environment.
•  Experience managing and developing data science teams and data science strategy.
•  Strong technical capability in Python, SQL and machine learning development.
•  Experience deploying batch and real-time models to production using modern engineering practices (CI/CD, versioning, automated testing).
•  Strong understanding of UK model‑risk regulatory expectations and proportionate governance approaches.
•  Excellent communication and stakeholder management skills.
•  Ability to communicate technical concepts to non‑technical audience.

Desirable

•  Experience in UK retail or SME banking.
•  Familiarity with real‑time or event‑driven data platforms (e.g., Kafka, Flink).
•  Experience in any of Docker, Airflow, YARN, MLFlow and BentoML
•  Experience in operational analytics, customer analytics or Economic‑Crime analytics.
•  Experience managing third‑party data or modelling vendors.