Vice President; Quantitative Finance Analyst

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 impacting 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! Job Description This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products. Responsibilities Design and develop traditional and advanced statistical and machine learning models to detect financial crimes and money laundering activities in bank products and channels. Perform large‑scale data mining and pattern recognition on complex, high‑dimensional datasets using SQL, SAS and Python to identify suspicious behaviors and transaction anomalies. Perform ongoing monitoring and model maintenance to ensure model performance, data integrity and coefficient stability to maintain model compliance. Conduct internet and external research to understand evolving scenarios in global financial crimes and leverage those findings in feature engineering (e.g., transaction velocity, relative transaction volume). Work (support and lead) with a cross‑functional team of data scientists, compliance experts and technologists to build cutting‑edge analytical solutions that strengthen the bank’s financial crime detection framework. Understand and execute activities that form the end‑to‑end model development and use life cycle. Ensure model governance and regulatory compliance by preparing thorough documentation, supporting model validation, remediating validation findings and facilitating audits aligned with FinCen, OCC and FRB guidelines. Drive innovation in AML model development by evaluating emerging AI/ML techniques, exploring external data sources, and contributing to strategic roadmap planning for money laundering detection capabilities. Required Skills & Experience Master’s degree or equivalent in Analytics, Mathematics, Statistics, Finance, or related field and 3 years of experience in the job offered or a related quantitative occupation. Must include 3 years of experience in each of the following: Developing predictive risk models for fraud and AML detection leveraging machine learning techniques such as deep learning using Python libraries (scikit‑learn, XGBoost, LightGBM, TensorFlow/PyTorch, featuretools, NetworkX), SAS, and SQL based feature extraction. Designing, training, and validating supervised and unsupervised statistical and machine learning models specifically for financial crime detection. Extracting, transforming, and analyzing large‑scale, complex datasets with a focus on identifying transaction anomalies and suspicious behavioral patterns using Pandas, NumPy, Spark/PySpark. Analyzing multi‑dimensional datasets to extract actionable insights for AML monitoring, sanctions screening, and fraud prevention using Python, R, SQL, Spark, Hadoop, and develop dashboards in data visualization tools like Tableau/Power BI. Utilizing expertise in global money laundering trends, including structuring, rapid fund movement, and check sequencing, and recognizing transactional patterns and behavioral indicators associated with illicit financial activity. Employer will accept pre or post‑Master’s degree experience. 10% domestic travel, as necessary. #J-18808-Ljbffr Bank of America