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Quantitative Finance Analyst

Jersey City, New Jersey;

Job Description:

Overview of Global Risk Analytics

Bank of America Merrill Lynch has an opportunity for a B5 Quantitative Finance Analyst within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM).  GRA is responsible for developing a consistent and coherent set of models and analytical tools 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 that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard.  GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks.  In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement and automation across all of these activities. 

Overview of Enterprise Risk Analytics

As a part of Global Risk Analytics (GRA), Enterprise Risk Analytics (ERA) is responsible for the development of cross-business holistic analytical models and tools. ERA consists of the following teams:

  • Economic Scenario Generation (ESG) provides consistent and granular scenario generation capabilities for economic and market variables that enable multiple “what-if” outcomes for government regulators and other business uses.

  • Enterprise Portfolio Analytics (EPA) provides portfolio surveillance visualization tools, utilizing advanced analytics (artificial intelligence/machine learning/natural language processing), to provide decision making support around the credit cycle, geo-intelligence, and thematic “what-if” analyses. EPA’s tools also support Enterprise strategic risk appetite and limits decisions for the bank’s risk and capital frameworks.

  • Concentration Risk provides capital estimates to support annual regulatory requirements and legal entity-level capital management using tools and techniques focused on identification, measurement, and mitigation of concentration risks across countries, regions, sectors, and industries.

  • Enterprise Capital Risk Analytics manages model performance monitoring and capital model issue resolution.

  • Compliance Modelling & Analytics supports Enterprise needs around Fair Lending and Global Financial Crimes Compliance.

  • Central Quantitative Group (CQG) provides sophisticated quantitative solutions for ERA clients. The group often partners with other teams within and outside GRA to provide these solutions.          

      

Overview of the Role

The candidate will have the opportunity to learn the latest developments in AML/Economic Sanctions, interact with industry-leading subject matter experts, apply an software engineering skills and development skills to meet business and regulatory requirements, and develop a career in this fast-paced and ever-changing world. This role demands a highly technical skillset, including analytical, programming, organizational, communication, and software design capabilities. This role will implement AML models in Python/PySparc for technology integration and use Hadoop/HIVE databases for model data input and output

Position Overview

Responsible for independently coding for analytics and complex modeling projects. Leads efforts in Python implementation of new models, analytic processes or system approaches. Participates in building out strategic model development infrastructure using Python and PySparc. Creates documentation for all activities and may work with technology staff in design of any system to run models developed. Incumbents possess excellent analytic/ quantitative and Python programming skills and are able to influence strategic direction and software design, as well as develop tactical plans.

Required Education, Skills, and Experience

  • Graduate degree in quantitative or STEM discipline (e.g. Mathematics, Economics, Engineering, Finance, Physics)

  • 2+ years professional experience in model implementation or statistical work or data analytics or quantitative research

  • 2+ years experience in programming in Python and  PySparc

  • 1+ year’s experience in developing on Hadoop/HIVE

  • 1+ year’s experience in developing with JIRA and Git

  • 1+ year’s experience in writing technical documentation and functional specs for software implementation

  • Strong analytical and problem-solving skills

  • Strong Software engineering skills and focus on using good software development practices

  • Strategic thinker that can use technical skills to solve business problems

Desired Skills and Experience

  • Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions

  • Experience with LaTeX

  • Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow

Job Band:

H5

Shift: 

1st shift (United States of America)

Hours Per Week:

40

Weekly Schedule:

Referral Bonus Amount:

2000

Job Description:

Overview of Global Risk Analytics

Bank of America Merrill Lynch has an opportunity for a B5 Quantitative Finance Analyst within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM).  GRA is responsible for developing a consistent and coherent set of models and analytical tools 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 that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard.  GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks.  In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement and automation across all of these activities. 

Overview of Enterprise Risk Analytics

As a part of Global Risk Analytics (GRA), Enterprise Risk Analytics (ERA) is responsible for the development of cross-business holistic analytical models and tools. ERA consists of the following teams:

  • Economic Scenario Generation (ESG) provides consistent and granular scenario generation capabilities for economic and market variables that enable multiple “what-if” outcomes for government regulators and other business uses.

  • Enterprise Portfolio Analytics (EPA) provides portfolio surveillance visualization tools, utilizing advanced analytics (artificial intelligence/machine learning/natural language processing), to provide decision making support around the credit cycle, geo-intelligence, and thematic “what-if” analyses. EPA’s tools also support Enterprise strategic risk appetite and limits decisions for the bank’s risk and capital frameworks.

  • Concentration Risk provides capital estimates to support annual regulatory requirements and legal entity-level capital management using tools and techniques focused on identification, measurement, and mitigation of concentration risks across countries, regions, sectors, and industries.

  • Enterprise Capital Risk Analytics manages model performance monitoring and capital model issue resolution.

  • Compliance Modelling & Analytics supports Enterprise needs around Fair Lending and Global Financial Crimes Compliance.

  • Central Quantitative Group (CQG) provides sophisticated quantitative solutions for ERA clients. The group often partners with other teams within and outside GRA to provide these solutions.          

      

Overview of the Role

The candidate will have the opportunity to learn the latest developments in AML/Economic Sanctions, interact with industry-leading subject matter experts, apply an software engineering skills and development skills to meet business and regulatory requirements, and develop a career in this fast-paced and ever-changing world. This role demands a highly technical skillset, including analytical, programming, organizational, communication, and software design capabilities. This role will implement AML models in Python/PySparc for technology integration and use Hadoop/HIVE databases for model data input and output

Position Overview

Responsible for independently coding for analytics and complex modeling projects. Leads efforts in Python implementation of new models, analytic processes or system approaches. Participates in building out strategic model development infrastructure using Python and PySparc. Creates documentation for all activities and may work with technology staff in design of any system to run models developed. Incumbents possess excellent analytic/ quantitative and Python programming skills and are able to influence strategic direction and software design, as well as develop tactical plans.

Required Education, Skills, and Experience

  • Graduate degree in quantitative or STEM discipline (e.g. Mathematics, Economics, Engineering, Finance, Physics)

  • 2+ years professional experience in model implementation or statistical work or data analytics or quantitative research

  • 2+ years experience in programming in Python and  PySparc

  • 1+ year’s experience in developing on Hadoop/HIVE

  • 1+ year’s experience in developing with JIRA and Git

  • 1+ year’s experience in writing technical documentation and functional specs for software implementation

  • Strong analytical and problem-solving skills

  • Strong Software engineering skills and focus on using good software development practices

  • Strategic thinker that can use technical skills to solve business problems

Desired Skills and Experience

  • Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions

  • Experience with LaTeX

  • Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow

Shift:

1st shift (United States of America)

Hours Per Week: 

40

Learn more about this role

Full time

JR-22036291

Band: H5

Manages People: No

Travel: No

Manager:

Talent Acquisition Contact:

Jillian Teeter

Referral Bonus:

2000