Quantitative Engineer Analyst

About the Role

The Quantitative Engineer Analyst role is responsible for understanding bank processes, including underlying data, flows, and controls, and designing and implementing technical solutions to enhance Global Risk Management’s (GRM) data, testing, and analytical capabilities.

Key Responsibilities

• Collaborate with process owners, risk managers, and technologists to assess current state processes, underlying data, process flows, and risks.
• Build domain-specific expertise in bank processes and data.
• Apply a combination of software engineering, big data, data science, and risk management skills to drive technical solution implementation and automate processes.
• Apply analytical thinking, critical thinking, sound judgment, and risk management principles to understand business processes, controls, and risks to influence appropriate designs for technical solutions.
• Partner with process owners, data owners, Front Line Units, Technology teams, and other stakeholders to understand business requirements and build understanding of the firm’s processes and technology landscape.
• Leverage programming expertise, software development lifecycle (SDLC) principles, and AI development patterns to build and document scalable analytical solutions that support business objectives, regulatory requirements, and operational efficiency.
• Source, validate, and analyze data while designing scalable data pipelines and analytical solutions across large and complex datasets.
• Apply quantitative and analytical techniques to identify trends and assess risk.
• Understand governance, security, observability, and auditability control requirements for AI-enabled solutions, including continuous monitoring of performance.
• Develop understanding on how to drive process optimization and enhance solution effectiveness through testing, feedback loops, automation, and responsible AI adoption.

Requirements & Tech Stack

• Programming expertise
• Software development lifecycle (SDLC) principles
• AI development patterns
• Data pipelines
• Quantitative and analytical techniques
• AI-enabled solutions
• Python
• Large language models (LLMs)
• Agentic AI concepts
• AI-assisted development tools
• Structured and unstructured data
• SQL
• APIs
• Databases

Qualifications

• Analytical Thinking & Problem Solving: Applies structured thinking, quantitative analysis, and sound judgment to solve business, risk, data, and technology challenges.
• Communication & Stakeholder Management: Effectively communicates complex technical concepts to different audiences and builds strong partnerships across stakeholders.
• Software Engineering & AI Development: Demonstrated strong programming skills (e.g., Python), analytical and problem-solving, digital fluency, and exposure to AI technologies, including large language models (LLMs), agentic AI concepts, or AI-assisted development tools through coursework, internships, personal projects, or professional experience.
• Data Engineering & Analytics: Experience working with structured and unstructured data, SQL, APIs, and databases to source, analyze, and integrate data while solving analytical problems.
• Analytical Thinking
• Critical Thinking
• Oral Communications
• Prioritization
• Written Communications
• Attention to Detail
• Change Management
• Collaboration
• Presentation Skills
• Bachelor’s degrees or above in fields including but not limited to: Mathematics, Computer Science, Statistics, Process and Mechanical Engineering, Operations Research, Data Science (or equivalent work experience).