Assistant Vice President; Quantitative Investment Analyst

Bank of America; New York (NY); On-site; USD 80,000 - 120,000; Full time

Job summary

A leading bank is seeking a Quantitative Analyst to enhance their investment and wealth management solutions. The role involves developing quantitative models, performing data analytics, and automating data processes using tools like Python and R. Ideal candidates will have a Master's in related fields and significant experience in quantitative analysis and machine learning.

Qualifications

• 2 years of experience in quantitative analytics, including machine learning and econometrics.
• Proficiency in Python (NumPy, SciPy, pandas) and R for data-driven decision making.
• Experience with classical statistical methods and machine learning models.

Responsibilities

• Develop and maintain investment and wealth management solutions.
• Monitor multiple asset portfolios and conduct quantitative analytics.
• Design innovative investment strategies leveraging advanced statistical methods.

Skills

Quantitative modeling

Optimization

Statistics

Machine learning

Data science

Education

Master's degree in Finance, Statistics, Mathematics, or related

Tools

Python

R

Matlab

Job description

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.

Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being a diverse and inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

RESPONSIBILITIES:

Deliver goals-based investment and wealth management solutions by developing and maintaining robust frameworks, services and tools, quantitative asset allocation and portfolio construction, with analytical tools of Python (NumPy, SciPy, pandas).

Monitor discretionary single asset and multi asset portfolios with tools such as Factset, Risk Metrics, Bloomberg and Morningstar Direct.

Design robust and innovative quantitative investment strategies, rules-based model portfolios and validated analytical models scale leveraging statistics and data science techniques such as machine learning to help clients achieve their financial goals across GWIM channels (Merrill, Edge, Institutional, Private Bank, Retirement & Personal Wealth Services).

Conduct quantitative analytics on models and portfolios performance leveraging mathematical and innovative problem-solving skills with demonstrable interest in prototyping and delivering proof of concept while evaluating alternative solutions.

Communicate and give presentations in an audience-appropriate manner with the ability to translate complex quantitative concepts into common-sense terms and thinking.

Perform quantitative modeling and analytics, including optimization, probability, statistics, econometrics, applied mathematics, machine learning, and differential equations and Copula theory.

Design and develop high-quality code and tools in Matlab, Python (NumPy, SciPy, and Pandas), and R to automate data extraction, processing, and data-driven decision-making.

Leverage classical statistical methods and advanced machine learning approaches of tree-based models, lasso regression, and principal component analysis to process complex data in real-time environments.

Work extensively with large datasets and integrating multiple data sources to generate data science solutions and actionable business insights.

REQUIRED SKILLS & EXPERIENCE:

Master's degree or equivalent in Finance, Statistics, Mathematics, or related: and

2 years of experience in the job offered or a related Quantitative occupation.

Must include 2 years of experience in each of the following:

Performing quantitative modeling and analytics, including optimization, probability, statistics, econometrics, applied mathematics, machine learning, and differential equations and Copula theory;

Designing and developing high-quality code and tools in Matlab, Python (NumPy, SciPy, and Pandas), and R to automate data extraction, processing, and data-driven decision-making;

Leveraging classical statistical methods and advanced machine learning approaches of tree-based models, lasso regression, and principal component analysis to process complex data in real-time environments; and,

Working extensively with large datasets and integrating multiple data sources to generate data science solutions and actionable business insights.