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 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! 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. This job is responsible for leveraging advanced analytics, artificial intelligence, and large-scale enterprise data to deliver innovative solutions that enhance Enterprise Process Management (EPM), process intelligence, and the Single Process Inventory (SPI). Key responsibilities include analyzing and interpreting complex datasets, developing semantic data models and AI-driven data products, and applying Generative AI and machine learning techniques to improve process discovery, governance, insights, and decision-making across the enterprise. The role partners closely with business and technology stakeholders to understand challenges, design scalable data and AI solutions, and translate analytical findings into actionable recommendations. Job expectations include demonstrating leadership, resilience, accountability, a disciplined approach to delivery, and a commitment to driving responsible growth through innovation and data-driven decision-making.

Requirements

5+ years of experience in data science, advanced analytics, machine learning, or artificial intelligence solution development.
5+ years of experience designing and developing enterprise data products and data-driven applications.
10+ years of financial services, technology, or large enterprise experience.
Strong experience with Python, SQL, and modern data engineering and analytics platforms.
Strong experience leveraging data to drive impactful business insight
Demonstrated ability to communicate complex AI, machine learning, and data science concepts to non-technical audiences in a clear and actionable manner
Experience developing and deploying machine learning models, AI applications, or Generative AI solutions in a production environment
Experience working with large-scale structured and unstructured datasets
Strong understanding of semantic data modeling, metadata management, data governance, and enterprise data management principles
Exceptional analytical, problem-solving, and critical-thinking skills
Demonstrated ability to work across business, product, and technology organizations to deliver measurable outcomes

Nice To Haves

Experience developing GenAI solutions utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, semantic search, or knowledge graph technologies
Experience delivering AI-enabled solutions for process management, process intelligence, workflow automation, or operational excellence initiatives
Familiarity with Enterprise Process Management platforms, process repositories, or process governance frameworks
Experience building reusable data products, APIs, and enterprise analytics capabilities
Exceptional analytical, problem-solving, organizational, and product development skills
Strong collaboration, stakeholder management, and executive communication skills
Experience leading cross-functional initiatives from ideation through production deployment
Passion for innovation and applying emerging AI capabilities to solve complex business challenges

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
Enables advanced business analytics through data exploration, semantic data modeling, trend analysis, process intelligence, and pattern recognition to generate actionable insights and support enterprise decision-making.
Designs, develops, and deploys data science products and Generative AI solutions that enhance Enterprise Process Management, Single Process Inventory capabilities, and process governance outcomes.
Develops and maintains semantic data structures, knowledge representations, metadata frameworks, and reusable data products that improve data quality, discoverability, and business value.
Applies Agile practices for product management, solution development, deployment, testing, and ongoing enhancement of AI and analytics capabilities.
Develops and reviews technical documentation, business requirements, solution architectures, model specifications, and implementation artifacts required for production deployment.
Manages multiple priorities and ensures quality and timeliness of deliverables, including quantitative models, GenAI applications, data products, analytical solutions, and executive-level insights.
Delivers presentations and communicates complex technical concepts, AI capabilities, and analytical findings effectively to a diverse set of business and technology stakeholders.
Collaborates with cross-functional teams, including process owners, product managers, architects, and engineering teams, to deliver scalable enterprise solutions.
Supports the identification of potential risks, data quality issues, model governance requirements, and development of appropriate controls.
Maintains expertise in emerging advancements across artificial intelligence, Generative AI, machine learning, semantic technologies, knowledge graphs, and enterprise data management to continuously improve business outcomes.