Data Scientist I

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 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.

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

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: 

THOR Data Engineering develops and maintains five key financial transaction Enterprise Information Products (EIPs)—THOR (Transaction History Opportunities Recognition), Non-Financial Activity (NFACT), THOR Global Wealth and Investment Management (GWIM), Private Bank (THORPB), and Sentinel THOR (STHOR)—within the THOR Authorized Data Source (ADS). The team also transforms Bank of America transaction data into enterprise data products, analytics, and economic insights that support risk management, modeling, and business decision-making. 

 
The THOR Data Engineering team is seeking a quantitative and detail-oriented Data Scientist to contribute to the continued development and maintenance of the THOR information product. This individual will support defined components of production enhancements, data analysis, quality assurance, and user support activities while developing a broad understanding of THOR data, processes, and business applications.

Working with guidance from more experienced team members, this individual will use SQL and other analytical tools to investigate transaction data, identify data nuances and anomalies, support the development and auditing of value-added attributes, and validate changes before and after production implementation. The role will collaborate with teammates and business partners to build knowledge of bank products, services, technology systems, and external marketplace activity that may affect the financial transaction data captured in THOR. The role requires the ability to understand use cases of transactional data and to craft data solutions that enable revenue generation, risk management, operational efficiency, regulatory compliance, portfolio management, and research performed by analysts enterprise wide.

The individual will also support analytical requests from the THOR user community and contribute to training materials, technical documentation, and informational presentations. Success in this role requires attention to detail, intellectual curiosity, an interest in transactional data, and the ability to apply established analytical and development practices to clearly defined assignments.

Key Responsibilities:

• Contribute to the continued development and maintenance of the THOR information product. 
• Build working knowledge of THOR database structures, data sources, value-added attributes, and production processes. 
• Develop an understanding of the distributions, patterns, and data nuances within the 2 billion transactions housed by THOR each month and processed on a daily basis. 
• Support the analysis and evaluation of proposed product enhancements for production releases, while bringing a “learning laboratory” approach to solving data problems. 
• Apply established quality-control practices and maintain careful attention to detail throughout analysis, development, testing, documentation, and post-production validation.
• Investigate data anomalies and communicate findings, questions, risks, and potential opportunities to the project team and appropriate business partners. 
• Evaluate alternative approaches for transaction categorization and other THOR value-added attributes using established research and testing methods. 
• Support ad hoc analytical requests, user documentation, training materials, and informational presentations for the THOR user community. 
• Collaborate with team members and cross-functional partners to complete assigned deliverables and resolve data or development issues.

Required Skills:

• Data Analysis
• Data Cleansing
• Data Quality and Validation
• Agile Practices
• Technical Documentation
• Written Communications
• Research and Analytical Methods
• Data management
• Data Visualization
• Presentation Skills
• Risk Awareness
• Adaptability
• Problem Solving and ability to find creative solutions to data problems
• Strong attention to detail and curiosity about data patterns and anomalies
• Understanding of BofA Products, Policies, Procedures, and Guidelines
• Ability to collaborate effectively within and between cross-functional teams 
• Awareness of the US marketplace and interest in major industries, consumer brands and financial transaction trends.

Required Technical Skills:

• 2-5 years of experience with data science/feature engineering/data engineering
• Strong programming skills in SQL/SAS along with experience working with Teradata table structures
• Bachelor’s degree in Math, Economics, Statistics, Engineering, Finance, Computer Science or similar discipline or related work experience is preferred
• Familiarity with statistical and non-statistical analytical techniques
• Experience with research methodology/design of experiments
• Ability to work with incomplete/ambiguous information, organize available facts, and discern subtle patterns where others might sense only noise
• Ability to integrate diverse information sources, tools and techniques into production processes

Skills:

• Adaptability
• Attention to Detail
• Business Analytics
• Technical Documentation
• Written Communications
• Agile Practices
• Application Development
• Collaboration
• Data Visualization
• DevOps Practices
• Artificial Intelligence/Machine Learning
• Networking
• Policies, Procedures, and Guidelines Management
• Presentation Skills
• Risk Management

Shift:

1st shift (United States of America)

Hours Per Week: 

40