Data Scientist

About the role

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