Senior Data Scientist - Enterprise Credit

Bank of America•Charlotte, NC

1d•Onsite

About The Position

At Bank of America, the Senior Data Scientist supports Responsible Growth by coordinating, reviewing, and optimizing data analysis to create revenue-generating opportunities and develop effective risk-management strategies. The role works with lines of business to identify and diagnose problems, apply sophisticated analytics and advanced techniques, and communicate recommendations based on findings. It calls for leadership, influence, accountability, and a commitment to responsible enterprise growth. The position focuses on evaluating proof-of-concepts for Enterprise Credit business cases using emerging AI technologies, assessing their strategic alignment, feasibility, scalability, and value across the model portfolio. It also supports Bank policy for AI models by identifying and mitigating risks associated with advanced techniques. The Senior Data Scientist mentors junior data scientists and analysts and contributes to an environment of continuous learning and innovation. The role is based at Bank of America Tower in Chicago, Illinois. Bank of America describes its culture as in-office, with flexibility determined by role responsibilities and business needs. The position is listed as first shift and 40 hours per week. The annual salary range is $125,000 to $187,500, with offers determined by experience, education, and skill set. The role is eligible for an annual discretionary incentive plan and benefits, including access to paid time off.

Requirements

At least 5 years of work experience in data science or a related field.
Strong programming skills in Python and SQL, with the ability and willingness to learn.
Interest in using data for business insight and experience building and deploying models.
Project management skills, including prioritization, meeting deadlines, and following through on high-profile projects or initiatives.
Ability to collaborate, demonstrate initiative and a strong work ethic, and balance multiple efforts in a fast-paced environment.
Ability to lead and influence stakeholders across levels and organizations, aligning technical teams and business stakeholders to deliver actionable insights.
Ability to identify and remediate risks in a timely manner and navigate the enterprise to source information across multiple functions.
Strong verbal and written communication skills.
General knowledge of Enterprise Credit businesses, processes, systems, and policies.
Experience building and implementing NLP solutions from scratch.
Experience with Python packages such as pandas, NumPy, scikit-learn, spaCy, NLTK, PyTorch, TensorFlow, or other advanced scientific, machine-learning, or NLP packages.
Hands-on experience with large language models, including prompt engineering, fine-tuning, or parameter-efficient adaptation such as LoRA or adapters for enterprise use cases.
Strong understanding of transformers, embeddings, attention mechanisms, and vector similarity search.
Experience operationalizing NLP or machine-learning solutions, including model validation, monitoring, drift detection, and post-deployment improvement.
Strong understanding of model risk, bias, explainability, and governance, particularly in regulated financial environments.
Experience mentoring junior data scientists and providing technical guidance on NLP, modeling best practices, and code quality.
High school diploma, GED, secondary school qualification, or equivalent.

Nice To Haves

6–10 or more years of experience in data science or a related field.
Experience integrating NLP solutions with structured and unstructured enterprise data sources, including APIs, databases, document stores, and search indices.
Experience handling model inputs and outputs at scale, including chunking strategies, embedding pipelines, and production latency and performance tradeoffs.
Experience designing multi-step or agentic workflows in which NLP models use tools, data sources, or intermediate outputs to complete complex tasks, such as retrieval-augmented generation or planning-execution loops.
Demonstrated success applying NLP or LLM-based solutions in document-heavy areas such as credit, risk, compliance, policy interpretation, or underwriting.
Experience supporting proof-of-concept-to-production transitions while balancing research innovation with enterprise scalability and controls.
Graduate degree in Data Science, Engineering, Computer Science, Quantitative Theory and Methods, Statistics, Mathematics, Management Science, Economics, Econometrics, Operational Research, Mathematics joint Political Science, or a similar quantitative discipline.

Responsibilities

Coordinate and review data collection, trend identification, and pattern recognition using advanced techniques to support decisions and identify data-driven insights.
Promote adoption of enterprise information products by clearly explaining how they answer material banking questions and lead to decisions and actions.
Apply agile practices to project management, solution development, deployment, and maintenance.
Oversee and support technical documentation of business requirements and specifications for analytical solutions and their production implementation.
Review quantitative models, data science products, analysis reports, and visualizations for quality and timely delivery, working independently and collaboratively.
Identify potential issues and develop controls to mitigate risk.
Apply knowledge of advances in data science and artificial intelligence to support business analytics.
Evaluate proof-of-concepts for Enterprise Credit business cases using emerging AI technologies, assessing strategic alignment, feasibility, scalability, and value across the model portfolio.
Support Bank policy for AI models and identify and mitigate risks associated with advanced techniques.
Mentor junior data scientists and analysts, providing technical guidance and fostering continuous learning and innovation.
Build and deploy models and NLP solutions, and communicate actionable insights to business and technical stakeholders.
Document assumptions, limitations, and controls for NLP and generative AI solutions for senior stakeholders and risk partners.