Quantitative Engineer - Consumer & Wholesale
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
- 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
- A minimum of 1-2 years relevant professional experience or evidence of personal projects and endeavours that show a passion for coding and problem solving.
- Strong Programming skills (e.g., Python) and solid understanding of Software Development Life cycle principles
- 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
- Strong Python development skills (including Pandas and related data-processing libraries).
- Experience with big data technologies such as Spark, PySpark, Hadoop, and Hive.
- 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)
Nice To Haves
- Exposure to quantitative modeling or financial modeling is a plus, but not required.
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
Benefits
- access to paid time off
- resources and support to our employees
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
