This job is responsible for analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies. It involves collaborating with key stakeholders to comprehend business problems, utilizing data gathering and analysis techniques to devise solutions, and presenting recommendations based on findings. The role requires flexibility, resilience, accountability, a disciplined approach, and a commitment to fostering responsible growth for the enterprise.
Responsibilities
Perform 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
Apply agile practices for project management, solution development, deployment, and maintenance
Create and maintain technical documentation, capturing the business requirements and specifications related to the developed analytical solution and its implementation in production
Manage multiple priorities and maintain 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
Deliver engaging presentations and engage in both in-person and virtual conversations that effectively communicate technical concepts and analysis results to a diverse set of internal stakeholders, and develop professional relationships to foster collaboration on work deliverables
Mitigate risk by identifying potential issues and developing controls
Research 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
Requirements
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
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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
Nice to have:
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
Important Details
Shift: 1st shift
Hours Per Week: 40
About the Company
At Bank of America, the company is guided by a common purpose to help make financial lives better through the power of every connection, driving Responsible Growth and delivering for clients, teammates, communities and shareholders every day. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development, with clear in-office expectations and appropriate flexibility based on role-specific responsibilities and business needs.
