Quantitative Engineer
About The Position
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 modeling 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.
Requirements
- Software engineering: modular code, software lifecycle processes, unit testing, regression testing
- Big data: distributed computing paradigms (e.g., mapreduce, dataframes, etc), optimizing distributed software
- Modeling / quantitative: basic modeling techniques (regression, classification, clustering, etc)
- Bachelor's degree in Computer Science, a closely related field, or a degree from a program where software engineering was a key focus or equivalent work experience
- At least 2 years of relevant experience in software engineering in Quantitative Finance or other industries
- Strong Programming skills (e.g., Python) and solid understanding of Software Development Life cycle principles
Nice To Haves
- Strong analytical and problem-solving skills
- Experience applying quantitative methods such as modelling, data analytics, machine learning, and statistics to develop business solutions
- Experience with large scale data sets with structured or unstructured data
- Experience in building user facing applications over large amounts of data using technologies like React, Angular, JavaScript etc.
- Experience implementing process improvements and automation
Responsibilities
- 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 modeling and testing business processes and requirements
- Think outside the box of current industry standards to develop innovative approaches
- 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 modeling 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
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Industry
Credit Intermediation and Related Activities
Number of Employees
5,001-10,000 employees
