Data Scientist II

Bank of America • Charlotte, NC

1d • Onsite

About The Position

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 is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Position Summary : The Global Operations Emerging Technology organization is building a next-generation data science capability to help Global Operations and Consumer (through Complaints AI) solve complex, high‑value business problems. This role focuses on applying analytical thinking, real‑world data science experience, and a strong business mindset to develop practical, scalable solutions that improve efficiency, reduce manual effort, strengthen controls, and enhance client and associate experiences. As a Data Scientist on this team, you will work across structured data, unstructured content, and operational workflows to frame the right problems, identify patterns and opportunities, and deliver models and analytical solutions that can be used in production environments. You will partner closely with Operations leaders, SMEs, engineers, and product teams to convert ambiguous issues into measurable outcomes—prioritizing solutions that are useful, reusable, and grounded in real operational value. You do not need prior experience with LLMs or specific enterprise tools, although previous real-world experience with data science projects, whether traditional statistics or machine learning is highly valued. What matters most is to have a foundation in practical data science, with a strong ability to think critically, ask sharp questions, work through messy datasets, and bring solutions from idea to impact. As part of a growing team, you will help shape best practices, influence our future technical direction, and contribute to an emerging culture of curiosity, rigor, and delivery excellence in Global Operations. This job is responsible for reviewing and interpretating large datasets to uncover revenue generation opportunities and ensuring the development of effective risk management strategies. Key responsibilities include working with lines of business to comprehend problems, utilizing sophisticated analytics and deploying advanced techniques to devise solutions, and presenting recommendations based on findings. Job expectations include demonstrating leadership, resilience, accountability, a disciplined approach, and a commitment to fostering responsible growth for the enterprise.

Requirements

• Minimum of 5 years of job related experience required .
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• Proven experience as a Data Scientist in Banking or a similar domain
• Proficiency in Python or R, and experience with data science libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
• Hands-on experience with large language models (e.g., Open AI GPT, Llama, or similar), including fine-tuning and prompt engineering.
• Strong knowledge of statistics, machine learning, and data mining techniques.
• Experience with data visualization tools (e.g., Tableau, Power BI).
• Experience with Big Data Platforms (Hadoop).
• Familiarity with SQL and working with relational databases.
• Excellent problem-solving, communication, and collaboration skills.
• Experience with cloud platforms (AWS, Azure, or GCP).

Nice To Haves

• Experience with NLP, deep learning, or time series analysis.
• Experience deploying models to production environments.
• Knowledge of regulatory requirements and compliance in banking and finance.
• Familiarity with MLOps practices and tools.
• Experience with Agile methodology and tools (JIRA or Rally)

Responsibilities

• Enables business analytics, including data analysis, trend identification, and pattern recognition, using advanced techniques to drive decision making and collection data driven insights
• Applies agile practices for project management, solution development, deployment, and maintenance
• Develops and reviews technical documentation, capturing the business requirements, and specifications related to the developed analytical solution and implementation in production
• Manages multiple priorities and ensures 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 presentations in an engaging and effective manner through in-person and virtual conversations that communicates technical concepts and analysis results to a diverse set of internal stakeholders, and develops professional relationships to foster collaboration on work deliverables
• Supports the identification of potential issues and development of controls
• Maintains knowledge of the latest advances in the fields of data science and artificial intelligence to support business analytics

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level