Data Scientist I

Bank of America•Atlanta, GA

4d•Onsite

About The Position

This job is responsible for analyzing and interpreting large datasets to uncover potential revenue generation opportunities and develop effective risk management strategies. Key responsibilities include collaborating with key stakeholders to comprehend business problems, utilizing data gathering and analysis techniques to devise solutions, and presenting recommendations based on the findings. Job expectations include demonstrating flexibility, resilience, accountability, a disciplined approach, and a commitment to fostering responsible growth for the enterprise. Overview of Global Risk Analytics: Global Risk Analytics (GRA) is a sub-line of business within Global Risk Management (GRM), responsible for developing a consistent and coherent set of models and analytical tools for effective risk and capital measurement, management and reporting across the bank. GRA is also responsible for model implementation, execution, forecasting and performance monitoring. The team drives innovation, process improvement and automation across these activities. Overview of Data Science for Technology, Risk and Operations: Data Science for Technology, Risk, and Operations (DSTRO) enables the Bank's responsible adoption of Artificial Intelligence (AI) through governance, model oversight, model development, and enterprise data engineering. By providing AI and model risk governance, AI-driven solutions, and critical transaction data assets, DSTRO delivers the controls, infrastructure, and insights needed to strengthen risk management, enhance operational effectiveness, and drive enterprise value. Team Overview: The Data Science for Technology, Risk, and Operations (DSTRO) Innovations team leverages Artificial Intelligence, Large Language Models (LLMs), and advanced analytics to improve efficiency and effectiveness across Global Compliance and Operational Risk, Global Risk Analytics, and broader risk and technology domains. The team focuses on automation, model support and governance, and innovative solutions that enhance risk management and operational processes. Role Description: As a Data Scientist I, you will support the development and deployment of data science, machine learning, and AI-driven solutions that address key business challenges across the enterprise. You will work on a variety of automation, analytics, and modeling initiatives while partnering with stakeholders across Global Risk Analytics (GRA), Global Technology, Global Compliance & Operational Risk (GCOR), and other business teams. This role provides an opportunity to apply Large Language Models, machine learning techniques, and modern software engineering practices to real-world business problems. The successful candidate will contribute to the design, development, testing, and deployment of data science and AI solutions that deliver measurable business impact across risk, compliance, and technology organizations.

Requirements

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
1+ years of experience applying data science, machine learning, analytics, or automation techniques to solve business problems (including internships or equivalent experience where appropriate).
Strong analytical, problem-solving, and communication skills.
Ability to work effectively in a cross-functional environment and translate business requirements into technical solutions.
Experience managing multiple priorities and delivering quality work in a fast-paced environment.
Experience working in Agile delivery environments and using project management tools such as Jira.
Experience developing solutions using Python and SQL.
Experience with Large Language Models (LLMs) and associated frameworks and tools, including: (1) LangChain, (2) Model Context Protocol (MCP), (3) Retrieval Augmented Generation (RAG), (4) Vector databases, and (5) LangGraph
Experience with Git and modern software development practices.
Experience working with CI/CD pipelines and version control workflows.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively in a collaborative, cross-functional environment

Nice To Haves

Experience with machine learning frameworks such as scikit-learn, PyTorch, or TensorFlow.
Experience building GenAI applications, copilots, agents, or workflow automation solutions.
Familiarity with model governance, model risk management, or AI governance frameworks.
Experience working with REST APIs, microservices, and enterprise application integration.
Experience with software testing, code reviews, and development best practices.
Experience in financial services, risk management, compliance, audit, or operational processes.
Experience using modern AI development tools such as GitHub Copilot and VS Code

Responsibilities

Performs business analytics, which includes data analysis, trend identification, and pattern recognition, using advanced techniques (e.g., machine learning, text mining, statistical analysis, etc.) to support decision-making and drive data-driven insights
Applies agile practices for project management, solution development, deployment, and maintenance
Creates and maintains technical documentation, capturing the business requirements and specifications related to the developed analytical solution and its implementation in production
Manages multiple priorities and maintains 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 engaging presentations and engages in both in-person and virtual conversations that effectively communicate technical concepts and analysis results to a diverse set of internal stakeholders, and develops professional relationships to foster collaboration on work deliverables
Mitigates risk by identifying potential issues and developing controls
Researches the latest advances in the fields of data science and artificial intelligence to support business analytics