Senior Data Scientist

Bank of America•Charlotte, NC

7d•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! Job Description: This job is responsible for coordinating, reviewing, and optimizing data analysis to create revenue generating opportunities, and overseeing the development of effective risk management strategies. Key responsibilities include working with lines of business to identify and diagnose problems, utilizing sophisticated analytics, and deploying advanced techniques to devise solutions, and clearly communicating recommendations based on findings. Job expectations include demonstrating leadership, influence, accountability, and a commitment to fostering responsible growth for the enterprise.

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.

Nice To Haves

6-10+ years of work experience in Data Science or related field.
Proven ability to integrate NLP solutions with structured and unstructured enterprise data sources, including APIs, databases, document stores, and search indices.
Experience working with model inputs and outputs at scale, including chunking strategies, embedding pipelines, and latency/performance tradeoffs in production systems.
Experience designing multi‑step or agentic workflows, where NLP models reason across tools, data sources, or intermediate outputs to complete complex tasks (e.g., retrieval‑augmented generation, planning‑execution loops).
Demonstrated success applying NLP or LLM‑based solutions to document‑heavy domains such as credit, risk, compliance, policy interpretation, or underwriting.
Experience supporting proof‑of‑concept to production transitions, 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 other similar quantitative discipline.

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.