Quantitative Engineer

Overview

As a Quantitative Engineer in Global Risk, you will design and implement scalable data and analytics capabilities that support risk management and regulatory needs. You collaborate with modelers, risk managers, and technologists to shape data pipelines, models, and testing processes. You will work across the big data stack to deliver robust solutions that adapt to changing portfolios and market conditions. This role offers the chance to contribute to responsible growth and impact across the bank with cross-functional collaboration.

Pay / Benefits

• Private healthcare
• Annual health screen
• Pension plan
• Maternity/Paternity leave
• Flexible benefits
• Wellbeing resources and gym access

Responsibilities

• Develop and apply quantitative methods to meet risk and regulatory requirements
• Understand financial data schemas, flow, and data quality controls
• Build performant big data pipelines
• Deliver high-quality code across models and testing processes using SDLC principles
• Collaborate with stakeholders to model and test business processes
• Innovate beyond current industry standards to improve approaches
• Maintain and enhance capabilities to respond to portfolio and market changes
• Source and evaluate data for modelling and testing
• Design, develop, and implement models and tests
• Produce clear technical documentation for models and regulatory purposes

Key requirements

• Bachelor in Computer Science or equivalent with software engineering emphasis
• Several years of relevant experience or demonstrated coding passion
• Strong programming skills (Python) and SDLC understanding
• At least one of: strong analytics/problem-solving; experience applying quantitative methods; experience with large-scale data sets; experience building user-facing applications over data
• Experience with Pandas and data-processing libraries
• Experience with big data technologies (Spark, PySpark, Hadoop, Hive)

• Analytical and problem-solving abilities
• Collaborative stakeholder engagement
• Innovative and outcomes-driven mindset
• Python development
• Pandas and data-processing libraries
• Big data: Spark, PySpark, Hadoop, Hive

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