Job description

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 analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies. Key responsibilities include collaborating with key stakeholders to comprehend business problems, utilizing data gathering and analysis techniques to devise solutions, and presenting recommendations based on the findings. Job expectations include demonstrating flexibility, resilience, accountability, a disciplined approach, and a commitment to fostering responsible growth for the enterprise. Position Summary: Client Protection Technology (CPT ), focused on authentication technology for customer-facing applications, is in search of a Machine Learning Engineer or Data Scientist who can independently deliver complex classification models from data exploration through production implementation. The ideal candidate has strong hands-on experience with Python, feature engineering, model training, evaluation, tuning, validation, and deployment of machine learning solutions in technical environments. This person should be comfortable working directly with large datasets, building repeatable data pipelines, writing clean and maintainable code, troubleshooting model and data issues, documenting technical decisions, and partnering with engineering teams to integrate models into scalable production workflows. Responsibilities: • Performs business analytics, which includes data analysis, trend identification, and pattern recognition, using advanced techniques (e.g., machine learning, text mining, statistical analysis, etc.) to support decision-making and drive data-driven insights • Applies agile practices for project management, solution development, deployment, and maintenance • Creates and maintains technical documentation, capturing the business requirements and specifications related to the developed analytical solution and its implementation in production • Manages multiple priorities and maintains quality and timeliness of work deliverables such as quantitative models, data science products, data analysis reports, or data visualizations, while exhibiting the ability to work independently and in a team environment • Delivers engaging presentations and engages in both in-person and virtual conversations that effectively communicate technical concepts and analysis results to a diverse set of internal stakeholders, and develops professional relationships to foster collaboration on work deliverables • Mitigates risk by identifying potential issues and developing controls • Researches the latest advances in the fields of data science and artificial intelligence to support business analytics • Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data • Perform hands-on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development • Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring Required Qualifications: • Master’s or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, data science, or a related discipline • 5+ years of hands-on data science or machine learning engineering experience delivering models, pipelines, or analytics solutions in highly technical environments • Strong Python development experience, including experience with common data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, or similar frameworks • Deep understanding of supervised machine learning classification approaches, including feature engineering, model training, hyperparameter tuning, validation, threshold selection, and model performance evaluation • Experience working with large structured datasets, performing data cleansing, transformation, exploratory analysis, and building repeatable data preparation workflows. • Ability to evaluate classification models using metrics such as precision, recall, F1 score, ROC-AUC, confusion matrices, and false positive or false negative analysis • Experience packaging, versioning, testing, and integrating machine learning models or scoring logic into production or near-production application workflows • Strong technical communication and documentation skills, including the ability to explain model assumptions, feature logic, validation results, implementation details, and operational tradeoffs • Experience designing, deploying, or supporting machine learning models where inference must be delivered in real time or near real time, including low-latency scoring, performance optimization, and integration with production decisioning workflows • Understand explainable AI, model governance, responsible AI practices, bias evaluation, auditability, and risk controls for production machine learning systems • Experience using GenAI tools such as Copilot to create agents and develop effective prompts that support the completion of daily tasks Desired Qualifications: • Experience developing classification models for fraud, risk, cybersecurity, authentication, customer behavior, anomaly detection, or other high-volume decisioning use cases • Familiarity with MLOps practices, including model packaging, version control, automated testing, CI/CD pipelines, model monitoring, and reproducible deployment workflows • Experience with distributed data processing, cloud platforms, or large-scale analytics environments such as Spark, Databricks, Hadoop, AWS, Azure, or similar technologies Skills: • Adaptability • Attention to Detail • Business Analytics • Technical Documentation • Written Communications • Agile Practices • Application Development • Collaboration • Data Visualization • DevOps Practices • Artificial Intelligence/Machine Learning • Networking • Policies, Procedures, and Guidelines Management • Presentation Skills • Risk Management Shift: 1st shift (United States of America) Hours Per Week: 40

Requirements

Must-haves

At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Applies agile practices for project management, solution development, deployment, and maintenance
Creates and maintains technical documentation, capturing the business requirements and specifications related to the developed analytical solution and its implementation in production
Mitigates risk by identifying potential issues and developing controls
Researches the latest advances in the fields of data science and artificial intelligence to support business analytics
Design, build, train, validate, and tune supervised machine learning classification models using structured, behavioral, and transactional data
Perform hands-on data exploration, cleansing, transformation, and feature engineering to create reliable inputs for model development
Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring
Master’s or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, data science, or a related discipline
5+ years of hands-on data science or machine learning engineering experience delivering models, pipelines, or analytics solutions in highly technical environments
Strong Python development experience, including experience with common data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, or similar frameworks
Deep understanding of supervised machine learning classification approaches, including feature engineering, model training, hyperparameter tuning, validation, threshold selection, and model performance evaluation
Experience working with large structured datasets, performing data cleansing, transformation, exploratory analysis, and building repeatable data preparation workflows.
Ability to evaluate classification models using metrics such as precision, recall, F1 score, ROC-AUC, confusion matrices, and false positive or false negative analysis
Experience packaging, versioning, testing, and integrating machine learning models or scoring logic into production or near-production application workflows
Strong technical communication and documentation skills, including the ability to explain model assumptions, feature logic, validation results, implementation details, and operational tradeoffs
Understand explainable AI, model governance, responsible AI practices, bias evaluation, auditability, and risk controls for production machine learning systems
Experience using GenAI tools such as Copilot to create agents and develop effective prompts that support the completion of daily tasks
Adaptability
Attention to Detail
Business Analytics

Nice-to-haves

Experience developing classification models for fraud, risk, cybersecurity, authentication, customer behavior, anomaly detection, or other high-volume decisioning use cases
Familiarity with MLOps practices, including model packaging, version control, automated testing, CI/CD pipelines, model monitoring, and reproducible deployment workflows
Experience with distributed data processing, cloud platforms, or large-scale analytics environments such as Spark, Databricks, Hadoop, AWS, Azure, or similar technologies

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonagileaici/cdawsazure

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

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