Hybrid Senior Data Scientist

at Bank of America

• Job title: Senior Data Scientist
• Job type: Full Time
• Experience level: Senior
• Salary: $125,000 - $192,500 per year
• Degree requirement: High School Diploma
• Tech skills: Numpy, Pandas, Python, PyTorch, Scikit-Learn, SQL, Tensorflow
• Location requirements: Hybrid, Charlotte, United States

About the role

• Senior Data Scientist at Bank of America coordinating data analysis and developing risk management strategies. Working with teams to drive insights and generate revenue opportunities.

Responsibilities

• 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
• Evaluating Proof of Concepts for Enterprise Credit Business Cases leveraging emerging AI technologies, ensuring alignment with strategic priorities and assessing feasibility, scalability, and value across the model portfolio.
• Supporting Bank policy for Artificial Intelligence models and ensuring risks associated with advanced techniques are identified and mitigated
• Mentoring junior data scientists and analysts, fostering a culture of continuous learning and innovation.

Requirements

• 5+ years of work experience in a Data Science role or related field.
• Proven ability in learning and strong programming (Python, SQL) skills.
• Strong interest in using data for business insight and experience in building and deploying models.
• Strong desire to learn and willingness to acquire needed knowledge and skills.
• Demonstrated project management skills, including ability to prioritize, meet deadlines and follow through on completion of high-profile projects or initiatives.
• Positive attitude, willingness to collaborate, strong work ethic, and demonstrated personal initiative.
• Organized; able to effectively prioritize and balance multiple efforts in a fast-paced environment.
• Ability to lead and influence stakeholders across multiple levels and organizations, aligning technical teams and business stakeholders to deliver actionable insights.
• Ability to identify and remediate risks in a timely manner.
• Ability to navigate the enterprise / 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 Natural Language Processing (NLP) solutions from scratch.
• Experience using Python packages such as: pandas, NumPy, scikit-learn, spaCy, NLTK, PyTorch, TensorFlow or other advanced scientific/ML/NLP Python packages.
• Hands‑on experience with Large Language Models (LLMs), including prompt engineering, fine‑tuning, or parameter‑efficient adaptation (e.g., LoRA, adapters) for enterprise use cases (e.g., document understanding, semantic search, summarization, or decision support).
• Strong understanding of modern NLP architectures, including transformers, embeddings, attention mechanisms, and vector similarity search.
• Demonstrated experience operationalizing NLP/ML solutions, including model validation, monitoring, drift detection, and iterative improvement post‑deployment.
• Strong understanding of model risk, bias, explainability, and governance considerations, especially in regulated financial environments.
• Ability to document assumptions, limitations, and controls for NLP and generative AI solutions in a manner suitable for senior stakeholders and risk partners.
• Experience mentoring junior data scientists, providing technical guidance on NLP, modeling best practices, and code quality.

Benefits

• Health insurance
• 401(k) matching
• Flexible work arrangements
• Paid time off
• Annual discretionary awards