What you'd actually do

Design and develop traditional and advanced statistical and machine learning (Neural Network, XGBoost, Random Forest, Support Vector Machine etc.) 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 co-efficient 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 i.e., to identify variables and its transformations to be used in new model development (E.g., Transaction velocity, relative transaction volume etc.).

Work (support and lead) with 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.

Skills

Required

Master's degree or equivalent in Analytics, Mathematics, Statistics, Finance, or related: and 3 years of experience in the job offered or a related Quantitative occupation.
3 years of experience in 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 etc), 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

Nice to have

broad knowledge of financial markets and products

What the JD emphasized

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 etc), 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

Ensure model governance and regulatory compliance by preparing thorough documentation, support model validation, remediate validation findings and facilitate audits aligned with FinCen, OCC and FRB guidelines.

Other signals

developing new models

analytic processes

systems approaches

large scale data mining

pattern recognition

suspicious behaviors

transaction anomalies

model maintenance

model compliance

feature engineering

model development

cross-functional team

analytical solutions

financial crime detection

model development and use life cycle

model governance

regulatory compliance

model validation

audits

FinCen, OCC and FRB guidelines

emerging AI/ML techniques

external data sources

strategic roadmap planning

money laundering detection

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!

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 (Neural Network, XGBoost, Random Forest, Support Vector Machine etc.) 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 co-efficient 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 i.e., to identify variables and its transformations to be used in new model development (E.g., Transaction velocity, relative transaction volume etc.).
Work (support and lead) with 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, support model validation, remediate validation findings and facilitate 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: 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 etc), 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; and,
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 Masters degree experience.
10% domestic travel, as necessary.

If interested apply online at www.bankofamerica.com/careers or email your resume to bofajobs@bofa.com and reference the job title of the role and requisition number.

EMPLOYER: Bank of America N.A.

Shift:

1st shift (United States of America)

**Hours Per Week: **