Assistant Vice President; Quantitative Inv Analyst

Bank of America • New York, NY

10d • Onsite

About The Position

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 an 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. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Job Description: This job is responsible for delivering high-touch service and high-quality deliverables to portfolio managers and internal business partners. Key responsibilities include streamlining interactions among diverse systems, developing innovative analyses, models, and tools that provide accessible insights to senior leaders and portfolio managers for timely decision-making. Job expectations include daily oversight of portfolios, developing tools and dashboard, collaborating with portfolio managers and analysts, and performing data analysis and developing code in R, Python or other applications.

Requirements

• 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: Building and owning a multi-asset portfolio optimization engine in Python, leveraging the Gurobi optimizer to solve large-scale constrained optimization while satisfying risk budgets, managing transaction costs and turnover, and enforcing factor exposures, and generating periodic rebalance recommendations consumed by portfolio managers; Leveraging the Triple-Barrier Method from Advances in Financial Machine Learning to devise an innovative time series labeling strategy, thereby improving the robustness, interpretability, and predictive power of existing models; Designing, developing, and deploying an artifact-packaging and retrieval system in Python to manage research artifacts in a structured, secure, and efficient manner, eliminating long-standing issues with inconsistent packaging, storage waste, and artifact traceability; Developing and deploying a web-based dashboard in Python and JavaScript for artifact visualization and filtering to enable non-technical users to download and interact with backend data assets without manually browsing shared spaces; Establishing and maintaining crypto market research infrastructure that integrates Bloomberg, Morningstar Direct, and FactSet data via APIs with SQL, Python, and R to compute alpha, beta, and R-squared, compare volatility-matched returns of major cryptocurrencies against benchmarks, and deliver actionable insights; and, Implementing lasso regression, principal component analysis, neural networks, XGBoost, natural language processing for sentiment analysis, and numerical methods for stochastic and partial differential equations in Python (NumPy, SciPy, and Pandas), C++, and R to forecast market conditions and optimize investment strategies.

Responsibilities

• Deliver goals-based investment and wealth management solutions by developing and maintaining robust frameworks, services and tools (e.g., 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.
• Work closely and transparently as a team player and develop relationships with a wide variety of stakeholders and business partners across the Bank to design, implement, and validate models under tight deadlines.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level