Data Scientist

Bank of America • Plano, TX

14d • Onsite

About The Position

Bank of America’s Client Protection Technology (CPT), focused on authentication technology for customer-facing applications, is seeking a Machine Learning Engineer or Data Scientist who can independently deliver complex classification models from data exploration through production implementation. The role analyzes and interprets large datasets to identify potential revenue opportunities and develop risk management strategies. It works with stakeholders to understand business problems, develop data-driven solutions, and present recommendations based on findings.

The position involves hands-on work across the machine learning lifecycle, including exploring and preparing data, engineering features, building and evaluating supervised classification models, and integrating models into scalable production workflows. The successful candidate will build repeatable Python-based pipelines, troubleshoot model and data issues, document technical decisions, and partner with engineering teams. The role also requires sound communication, collaboration, risk awareness, adaptability, accountability, and a disciplined approach to delivering responsible growth.

Bank of America describes an in-office culture that supports collaboration, engagement, and career development, with flexibility based on role-specific responsibilities and business needs. The role is 1st shift and 40 hours per week.

Requirements

• Master’s or PhD in a quantitative or technical field such as computer science, statistics, mathematics, engineering, data science, or a related discipline.
• At least 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 familiarity with data science and machine learning libraries such as pandas, NumPy, scikit-learn, XGBoost, or similar frameworks.
• Deep understanding of supervised machine learning classification, including feature engineering, model training, hyperparameter tuning, validation, threshold selection, and performance evaluation.
• Experience working with large structured datasets, including data cleansing, transformation, exploratory analysis, and 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 explaining model assumptions, feature logic, validation results, implementation details, and operational tradeoffs.
• Experience designing, deploying, or supporting machine learning models with real-time or near-real-time inference, including low-latency scoring, performance optimization, and integration with production decisioning workflows.
• Understanding of 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 daily tasks.

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.

Responsibilities

• Perform business analytics, including data analysis, trend identification, and pattern recognition using advanced techniques such as machine learning, text mining, and statistical analysis.
• Apply agile practices to project management, solution development, deployment, and maintenance.
• Create and maintain technical documentation covering business requirements, solution specifications, and production implementation.
• Manage multiple priorities and deliver quality, timely quantitative models, data science products, analysis reports, or visualizations, working independently and with a team.
• Present technical concepts and analysis results to diverse internal stakeholders in person and virtually, and build professional relationships that foster collaboration.
• Identify potential issues and develop controls to mitigate risk.
• Research advances in 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.
• Explore, cleanse, and transform data, and engineer features to create reliable model inputs.
• Develop repeatable Python-based workflows, scripts, and pipelines for dataset preparation, model training, evaluation, and scoring.
• Package, version, test, and integrate machine learning models or scoring logic into production or near-production workflows.
• Support real-time or near-real-time model inference, including low-latency scoring, performance optimization, and integration with production decisioning workflows.

Benefits

• Bank of America states that it supports teammates’ physical, emotional, and financial wellness through affordable, competitive, and flexible benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree