Quantitative Engineer

• Software engineering: modular code, software lifecycle processes, unit testing, regression testing
• Big data: distributed computing paradigms (e.g., mapreduce, dataframes, etc), optimizing distributed software
• Modelling / quantitative: basic modelling techniques (regression, classification, clustering, etc)

Responsibilities

ROLE DESCRIPTION:

Quantitative engineers in Global Risk are responsible for designing and implementing common, reusable, and scalable software components. These components enable GRM's data and analytical capabilities. These components can be domain independent (e.g., generic data quality tools over trillions of rows of data) or domain specific (e.g., classification models for surveillance or testing framework for Global Markets processes). Quantitative engineers work with modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Quantitative engineers have a combination of software engineering, big data, and modelling skills and the ability to work across the entire spectrum of a big data stack - from data to logic to model to UI to UX., * Applying quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements

• Understanding financial data: schemas, flow, size, data issues, data controls, etc.
• Building performant big data pipelines
• Use programming skills and knowledge of software development lifecycle principles to deliver high quality code for model and testing processes
• Collaborate with key stakeholders across the Bank to understand modelling and testing business processes and requirements
• Maintaining and continuously enhancing capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks
• Source and evaluate data required for modelling and testing
• Design and develop and implement models and tests
• Produce clear, concise and repeatable technical documentation models and tests for internal and regulatory purposes

Regression Testing, Big Data, Unit Testing, Classification