Jersey City, New Jersey

Job Description:

Overview of Global Risk Analytics:
Bank of America has an opportunity for a Quantitative Finance Analyst within the Alternative Modelling Group & Quantitative Solutions (AMG-QS) of Global Risk Analytics (GRA). Global Risk Analytics (GRA) is a sub line of business within Global Risk Management (GRM). The GRA team provides quantitative capabilities supporting global risk management and capital management and develops a consistent set of risk and capital models and analytical tools that drive the company's technology infrastructure. The role will involve the development and enhancement of next generation of Risk and Capital models to improve the bank's risk management capability. This is a challenging and intellectually stimulating role in a dynamic team that is used to delivering in a timely manner to many different constituents of the bank.

Team Description:
The GRALIB team is a part of the Global Risk Analytics (GRA) organization. It is responsible for maintaining and expanding a shared software library focused on statistical methods and machine learning. GRALIB team members do not develop new models or algorithms, nor do they work as data scientists. Rather, they develop and maintain reusable tools that are used by both modelers and data scientists who work on other GRA teams.

Overview of the Role:
We are looking for technically minded problem solvers with the desire to work across a number of functional areas to drive the development of next generation risk and capital models; including champion and challenger, using traditional regression methods and next-generation modelling techniques.
  • Critical to the role is to be able to think outside the box of current industry standards to develop innovative approaches to modelling problems
  • Pro-actively work with stakeholders across the company to collect requirements and then identify and build modelling solutions to meet them; and effectively communicate those solutions to the stakeholders
  • Ensure that next generation models are ready for enhanced climate risk requirements
  • Provide insight and thought leadership into the development of new models, analytic processes or systems approaches
  • Promote the adoption of GRA best practices for model development, implementation and monitoring
  • Pro-actively work with stakeholders across the firm to identify opportunities to improve existing models/processes
  • Produce clear and coherent technical documentation for internal and regulatory purposes
  • Take ownership to deliver results and meet critical deadlines

Required Skills:
  • Highly numerical degree (Masters required and PhD level desirable) in Statistics, Financial Mathematics, Applied Mathematics, Economics, Physics or Engineering
  • At least intermediate-level knowledge of Python, with substantial practical experience in this area.
  • At least two years of experience developing and maintaining a reusable software library or some other component in a complex software system.
  • Understanding of basic principles of large-scale software development, best practices of programming and code maintenance, and systems design.
  • Working knowledge of common numeric algorithms and data structures.
  • Basic familiarity with object-oriented design and functional programming.
  • Prior work experience with econometrics, including both linear regression and MLE principles and techniques. However, we shall also consider candidates who lack such experience, but have very strong mathematical background otherwise.
  • Solid understanding of basic topics in statistics is highly desirable.
  • Practical experience with using Apache Spark is highly desirable.
  • If the candidate has never used Spark, he/she should at least have prior exposure to other types of large-scale distributed computing.

Desired Skills:
  • Experiences in the areas of credit risk modeling, operational risk modelling, loss forecasting etc. preferred
  • Knowledge of regulatory guidelines including CCAR, DFAST, CECL, DFAST, ICAAP.
  • Strong stakeholder engagement skills with an ability to work with colleagues in other functions (business, risk and model validation)
  • Organized, practical and execution focused with some project management experience
  • Self-motivated and intellectually curious about both the role, supporting technologies and the wider bank


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 has an opportunity for a Quantitative Finance Analyst within the Alternative Modelling Group & Quantitative Solutions (AMG-QS) of Global Risk Analytics (GRA). Global Risk Analytics (GRA) is a sub line of business within Global Risk Management (GRM). The GRA team provides quantitative capabilities supporting global risk management and capital management and develops a consistent set of risk and capital models and analytical tools that drive the company's technology infrastructure. The role will involve the development and enhancement of next generation of Risk and Capital models to improve the bank's risk management capability. This is a challenging and intellectually stimulating role in a dynamic team that is used to delivering in a timely manner to many different constituents of the bank.

Team Description:
The GRALIB team is a part of the Global Risk Analytics (GRA) organization. It is responsible for maintaining and expanding a shared software library focused on statistical methods and machine learning. GRALIB team members do not develop new models or algorithms, nor do they work as data scientists. Rather, they develop and maintain reusable tools that are used by both modelers and data scientists who work on other GRA teams.

Overview of the Role:
We are looking for technically minded problem solvers with the desire to work across a number of functional areas to drive the development of next generation risk and capital models; including champion and challenger, using traditional regression methods and next-generation modelling techniques.
  • Critical to the role is to be able to think outside the box of current industry standards to develop innovative approaches to modelling problems
  • Pro-actively work with stakeholders across the company to collect requirements and then identify and build modelling solutions to meet them; and effectively communicate those solutions to the stakeholders
  • Ensure that next generation models are ready for enhanced climate risk requirements
  • Provide insight and thought leadership into the development of new models, analytic processes or systems approaches
  • Promote the adoption of GRA best practices for model development, implementation and monitoring
  • Pro-actively work with stakeholders across the firm to identify opportunities to improve existing models/processes
  • Produce clear and coherent technical documentation for internal and regulatory purposes
  • Take ownership to deliver results and meet critical deadlines

Required Skills:
  • Highly numerical degree (Masters required and PhD level desirable) in Statistics, Financial Mathematics, Applied Mathematics, Economics, Physics or Engineering
  • At least intermediate-level knowledge of Python, with substantial practical experience in this area.
  • At least two years of experience developing and maintaining a reusable software library or some other component in a complex software system.
  • Understanding of basic principles of large-scale software development, best practices of programming and code maintenance, and systems design.
  • Working knowledge of common numeric algorithms and data structures.
  • Basic familiarity with object-oriented design and functional programming.
  • Prior work experience with econometrics, including both linear regression and MLE principles and techniques. However, we shall also consider candidates who lack such experience, but have very strong mathematical background otherwise.
  • Solid understanding of basic topics in statistics is highly desirable.
  • Practical experience with using Apache Spark is highly desirable.
  • If the candidate has never used Spark, he/she should at least have prior exposure to other types of large-scale distributed computing.

Desired Skills:
  • Experiences in the areas of credit risk modeling, operational risk modelling, loss forecasting etc. preferred
  • Knowledge of regulatory guidelines including CCAR, DFAST, CECL, DFAST, ICAAP.
  • Strong stakeholder engagement skills with an ability to work with colleagues in other functions (business, risk and model validation)
  • Organized, practical and execution focused with some project management experience
  • Self-motivated and intellectually curious about both the role, supporting technologies and the wider bank


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 has an opportunity for a Quantitative Finance Analyst within the Alternative Modelling Group & Quantitative Solutions (AMG-QS) of Global Risk Analytics (GRA). Global Risk Analytics (GRA) is a sub line of business within Global Risk Management (GRM). The GRA team provides quantitative capabilities supporting global risk management and capital management and develops a consistent set of risk and capital models and analytical tools that drive the company's technology infrastructure. The role will involve the development and enhancement of next generation of Risk and Capital models to improve the bank's risk management capability. This is a challenging and intellectually stimulating role in a dynamic team that is used to delivering in a timely manner to many different constituents of the bank.

Team Description:
The GRALIB team is a part of the Global Risk Analytics (GRA) organization. It is responsible for maintaining and expanding a shared software library focused on statistical methods and machine learning. GRALIB team members do not develop new models or algorithms, nor do they work as data scientists. Rather, they develop and maintain reusable tools that are used by both modelers and data scientists who work on other GRA teams.

Overview of the Role:
We are looking for technically minded problem solvers with the desire to work across a number of functional areas to drive the development of next generation risk and capital models; including champion and challenger, using traditional regression methods and next-generation modelling techniques.
  • Critical to the role is to be able to think outside the box of current industry standards to develop innovative approaches to modelling problems
  • Pro-actively work with stakeholders across the company to collect requirements and then identify and build modelling solutions to meet them; and effectively communicate those solutions to the stakeholders
  • Ensure that next generation models are ready for enhanced climate risk requirements
  • Provide insight and thought leadership into the development of new models, analytic processes or systems approaches
  • Promote the adoption of GRA best practices for model development, implementation and monitoring
  • Pro-actively work with stakeholders across the firm to identify opportunities to improve existing models/processes
  • Produce clear and coherent technical documentation for internal and regulatory purposes
  • Take ownership to deliver results and meet critical deadlines

Required Skills:
  • Highly numerical degree (Masters required and PhD level desirable) in Statistics, Financial Mathematics, Applied Mathematics, Economics, Physics or Engineering
  • At least intermediate-level knowledge of Python, with substantial practical experience in this area.
  • At least two years of experience developing and maintaining a reusable software library or some other component in a complex software system.
  • Understanding of basic principles of large-scale software development, best practices of programming and code maintenance, and systems design.
  • Working knowledge of common numeric algorithms and data structures.
  • Basic familiarity with object-oriented design and functional programming.
  • Prior work experience with econometrics, including both linear regression and MLE principles and techniques. However, we shall also consider candidates who lack such experience, but have very strong mathematical background otherwise.
  • Solid understanding of basic topics in statistics is highly desirable.
  • Practical experience with using Apache Spark is highly desirable.
  • If the candidate has never used Spark, he/she should at least have prior exposure to other types of large-scale distributed computing.

Desired Skills:
  • Experiences in the areas of credit risk modeling, operational risk modelling, loss forecasting etc. preferred
  • Knowledge of regulatory guidelines including CCAR, DFAST, CECL, DFAST, ICAAP.
  • Strong stakeholder engagement skills with an ability to work with colleagues in other functions (business, risk and model validation)
  • Organized, practical and execution focused with some project management experience
  • Self-motivated and intellectually curious about both the role, supporting technologies and the wider bank


Shift:
1st shift (United States of America)

Hours Per Week:
40
Learn more about this role

Jersey City, New Jersey

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As a leader in the military, you motivated troops to get the job done. We value your ability to influence change and encourage you to continue that influence here and in our communities. Our Military Affairs Team proudly supports veterans in our communities through education and volunteer events. Together, we can create better communities and a brighter future for us all. 

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