Quantitative Finance Analyst
Charlotte, North Carolina;Dallas, Texas; Chicago, Illinois; Atlanta, Georgia
Job Description:
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 thesesolutions.
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:
0
Job Description:
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 thesesolutions.
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-22099174
Band: H5
Manages People: No
Travel: No
Manager:
Talent Acquisition Contact:
Jillian Teeter
Referral Bonus:
0
Bank of America and its affiliates consider for employment and hire qualified candidates without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote the concept of equal employment opportunity and affirmative action, in accordance with all applicable federal, state, provincial and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our teammates.
To view the “EEO is the Law” poster, CLICK HERE (~~~) .
To view the “EEO is the Law” Supplement, CLICK HERE (~~~) .
Bank of America aims to create a workplace free from the dangers and resulting consequences of illegal and illicit drug use and alcohol abuse. Our Drug-Free Workplace and Alcohol Policy (“Policy”) establishes requirements to prevent the presence or use of illegal or illicit drugs or unauthorized alcohol on Bank of America premises and to provide a safe work environment.
To view Bank of America’s Drug-free workplace and alcohol policy, CLICK HERE .
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