Vice President; Structurer

Bank of America; New York (NY); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Bank of America seeks a seasoned quantitative researcher to design and implement volatility-driven strategies and risk analytics for institutional clients. You will build end-to-end modeling frameworks, back-testing pipelines, and risk analyses using Python within a collaborative, cross-functional environment.

The role emphasizes advanced timeseries modeling, ML/deep learning techniques, and strong communication to educate stakeholders on quantitative offerings and new strategies.

Qualifications

• Master's degree in Financial Engineering, Statistics, Finance, or related field.
• 3 years of experience in the job offered or a related Finance occupation.
• Three years of experience in advanced quantitative modeling and data science principles including timeseries analysis, ML and deep learning.
• Experience applying statistical, econometric, and predictive analytics to large financial datasets.
• Experience developing, back testing, and evaluating quantitative forecasting and portfolio analytics models using Python.

Responsibilities

• Develop and implement quantitative modeling frameworks to design, evaluate, and optimize systematic equity volatility strategies using time-series analysis and ML techniques.
• Research, construct, and evaluate volatility-based strategies using index options, single-stock options, and volatility-linked derivatives.
• Design and implement quantitative hedging strategies and portfolio hedging to deliver downside protection and optimize carry costs.
• Conduct scenario analysis, stress testing, and performance attribution to assess robustness and risk exposures.
• Develop and maintain quantitative research tools, models, and analytics pipelines for back-testing and risk analysis using Python.
• Support equity derivatives solutions for institutional clients by delivering quantitative insights and risk analytics through dashboards and marketing materials.
• Collaborate with sales, trading, and risk to translate business objectives into quantitative analytics solutions.

Skills

Timeseries analysis

Machine learning

Deep learning

Quantitative forecasting

Risk analytics

Education

Master's degree in Financial Engineering/Statistics/Finance

Tools

Python

Backtesting

Data pipelines

Pandas

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 and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.

We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.

Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.

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

Responsibilities

• Develop and implement quantitative modeling frameworks to design, evaluate, and optimize systematic equity volatility strategies, leveraging time-series analysis, statistical modeling, machine learning techniques, and regime-switching approaches.
• Research, construct, and evaluate equity volatility-based strategies, including yield-oriented volatility alpha and relative value strategies, utilizing index options, single-stock options, and volatility-linked derivatives to achieve defined risk-return objectives.
• Design and implement quantitative hedging strategies and portfolio hedging solutions to deliver targeted downside protection under adverse market conditions while optimizing carry costs and risk-adjusted performance.
• Conduct scenario analysis, stress testing, and performance attribution to assess strategy robustness, portfolio behavior, and risk exposures across varying market environments.
• Develop and maintain quantitative research tools, models, and analytics pipelines for back-testing, market risk analysis, and process automation using Python and related analytical frameworks.
• Support the development and positioning of equity derivatives solutions for institutional clients by delivering quantitative insights, modeling outputs, and risk analytics through client-facing dashboards and marketing materials.
• Collaborate with cross-functional teams, including sales, trading, and risk, to translate business objectives into quantitative modeling and analytics solutions, and educate stakeholders on existing offerings and new strategies.
• Remote work may be permitted within a commutable distance from the worksite.

Required Skills & Experience

• Master's degree or equivalent in Financial Engineering, Statistics, Finance, or related: and
• 3 years of experience in the job offered or a related Finance occupation.
• Must include 3 years of experience in each of the following:
• Utilizing advanced quantitative modeling and data science principles, including timeseries analysis, machine learning, and deep learning, to develop quantitative forecasting frameworks for volatility driven risk, drawdowns, and adverse portfolio outcomes;
• Applying statistical, econometric, and predictive analytics techniques to largescale financial datasets to extract insights, identify trends, quantify risk exposures, and assess portfolio level performance across different market regimes;
• Developing, back testing, and evaluating quantitative forecasting and portfolio analytics models, including scenario analysis and performance attribution, using Python in a production research environment;
• Designing and developing end to end quantitative research and analytics infrastructure, including data ingestion pipelines, feature engineering processes, and scalable simulation environments to enable robust risk analytics and quantitative strategy evaluation; and,
• Supporting risk management, and investment decision making by translating business objectives into actionable quantitative modeling solutions through collaboration with cross functional stakeholders.

EMPLOYER: BofA Securities, Inc.

Shift

1st shift (United States of America)

Hours Per Week

40