Senior Data Scientist

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!

• →Lead design, development, and deployment of AI applications across multiple business domains
• →Own delivery accountability across planning, execution, testing, and production rollout
• →Design and implement AI-powered solutions, including: (LLMs, prompt engineering, RAG pipelines
• →Evaluate and adopt emerging AI technologies (e.g., Copilot, Foundry, open-source frameworks)
• →Python (FastAPI, data pipelines, AI/ML frameworks)
• →Cloud-native patterns (Azure/AWS/GCP)
• →Ensure system observability (logging, monitoring, alerting)

• →Coordinates and reviews data collection, trend identification, and pattern recognition, using advanced techniques to drive decision making and identification of data driven insights
• →Contributes to the adoption of enterprise information products through communicating in a clear manner how enterprise information products answer material banking questions leading to decisions and actions
• →Applies agile practices for project management, solution development, deployment, and maintenance
• →Provides oversight and support of technical documentation, capturing the business requirements, and specifications related to the developed analytical solution, and implementation in production
• →Reviews work products to ensure adequate 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
• →Mitigates risk by identifying potential issues and developing controls
• →Possesses deep knowledge and subject-matter expertise of the latest advances in the fields of data science and artificial intelligence to support business analytics
• →Lead end-to-end delivery of AI and non-AI software solutions across multiple projects
• →Design and implement scalable architectures (microservices, cloud-native, event-driven)
• →Build and maintain backend systems using Java and Python
• →Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms

• 10+ years of experience in software engineering and system design
• Proven experience delivering large-scale enterprise applications and AI solutions

• Experience in financial services or regulated industries
• Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)
• Familiarity with agent orchestration, MCP, and AI platform integration patterns
• Experience with data privacy, compliance, and secure AI deployments

• Python (AI/ML, APIs, data engineering)
• Hands-on experience with:
• AI/ML frameworks (OpenAI, Hugging Face, LangChain, etc.)
• RAG pipelines, embeddings, vector databases
• RESTful APIs, distributed systems

• Microservices, APIs, event-driven architectures
• Cloud platforms (Azure preferred)
• Containerization (Docker, Kubernetes)

• LLM-based applications and prompt engineering
• Model lifecycle management (training, deployment, monitoring)
• AI governance, risk, and explainability (preferred in regulated industries)

• Strong problem-solving and analytical thinking
• Excellent communication and stakeholder management
• Ability to operate in a fast-paced, ambiguous environment

• Artificial Intelligence/Machine Learning
• Business Acumen
• Presentation Skills
• Project Management
• Technical Documentation
• Candidate Screening
• Data Visualization
• Policies, Procedures, and Guidelines Management
• Risk Management
• Written Communications
• Adaptability
• Career Path Design
• Consulting
• Performance Management
• Workforce Opportunity Management