Quantitative Engineer

Job Description:
This job is responsible for designing, developing, testing and implementing common, reusable, and scalable software components which are either domain independent (generic data quality tools over billions of rows of data) or domain specific (classification models for surveillance or testing framework for Global Markets processes). Key responsibilities include enabling Global Risk Management's data and analytical capabilities. Job expectations include working with modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics.

Responsibilities:

Applies quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements

Understands financial data: schemas, flow, size, data issues, data controls, etc.

Builds performant big data pipelines

Uses programming skills and knowledge of software development lifecycle principles to deliver high quality code for model and testing processes

Collaborates with key stakeholders across the Bank to understand modeling and testing business processes and requirements

Thinks outside the box of current industry standards to develop innovative approaches

Maintains and continuously enhances capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks

Global Risk Analytics (GRA) is a sub-line of business within Global Risk Management (GRM), responsible for developing a consistent and coherent set of models, analytical tools, and tests for effective risk and capital measurement, management and reporting across Bank of America. GRA partners with the Lines of Business and Enterprise functions to ensure the capabilities it builds address both internal and regulatory requirements, and are responsive to the changing nature of portfolios, economic conditions, and emerging risks. In executing its activities, GRA drives innovation, process improvement and automation.

Job 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 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.

Job Responsiblities:

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

Required Qualifications:
Candidates should meet all or a subset of the following technical skills:

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

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

Candidates should have at least one of these following skills and preferably have at least two of these skills:

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.

Exposure to quantitative modeling or financial modeling is a plus, but not required.

Skills:

Critical Thinking

Data Modeling

Process Effectiveness

Risk Modeling

Test Engineering

Influence

Oral Communications

Prioritization

Relationship Building

Written Communications

Attention to Detail

Change Management

Minimum Education Requirement: Bachelor’s degree in related field or equivalent work experience

Shift:

1st shift (United States of America)
Hours Per Week:

40