Senior Quantitative Engineer

Bank of America Corporation•New York, NY

55d•Onsite

About The Position

Senior Quantitative engineers in Global Risk are responsible for designing and overseeing the implementation of 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 billions of rows of data) or domain specific (e.g., classification models for surveillance or testing framework for Global Markets processes). Senior Quantitative engineers work with senior modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Senior Quantitative engineers have a combination of software engineering, big data, modeling and management 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.)
High level of intellectual curiosity
Experience implementing strategic process improvements and/or automation
Proven track record of building relationships and effectively influencing key stakeholders
Adept at detecting and remediating organizational / process weaknesses and deficiencies
Feels ownership and accountability for delivering high quality work, able to balance multiple priorities and meet strict deadlines
Strong written, verbal, presentation creation and delivery skills, typically to very senior audiences
Well organized with attention to detail
Willing to challenge the status quo and with a track record of driving change
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
7+ years of relevant work experience
At least 7 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
Proven track record of seamlessly leading and executing enterprise-wide initiatives
The candidate must be a strong communicator and able to effectively explain the rationale and details of the methodologies to a broad set of audiences that include key senior stakeholders across the Bank, as well as auditors and regulators
Strong analytical and problem-solving skills

Nice To Haves

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

Seek opportunities for consistent improvement in quality, efficiency, and processes
Partner effectively with senior stakeholders within GRA & EIT, Front Line Units, Technology, Audit and Compliance
Provide leadership and oversight to less experienced team members
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