Data Scientist I

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization.

Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Overview of Global Risk Analytics
Bank of America Merrill Lynch has an opportunity for a Data Scientist within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM). GRA is responsible for developing a consistent and coherent set of models and analytical tools for effective risk and capital measurement, management and reporting across Bank of America. GRA partners with the Lines of Business and Enterprise functions to ensure that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard. GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks. In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement, and automation across all of these activities.          

Overview of Consumer Risk
Consumer Risk is primarily responsible for:
• Oversight and delivery of key regulatory reviews such as the Current Expected Credit Losses (CECL) accounting standard and the Comprehensive Capital Analysis & Review (CCAR), as well as other strategic initiatives, including data and infrastructure development and maintenance
• Planning and delivery of a coherent model risk management framework and infrastructure across Consumer. These efforts include the development of one universal platform for seamless model development and implementation, and improvements to the quality and consistency of the data sourced for all development and production purposes
• Developing and maintaining risk and capital models and model systems across Consumer product lines. Models and model systems provide insight into various risk areas, including loan default, exposure at default (EAD), loss given default (LGD), delinquency, prepayment, balances, pricing, risk appetite, revenues and cash flows
• Developing and implementing quantitative solutions on strategic Consumer Risk platforms. Outputs include GRA libraries that perform consumer risk model calculations, analytical tools, processes and documentation
• Conducts research and analysis to improve understanding and assessment of loan portfolios, models used, and forecast results
• Partners with Consumer lines of business, and front line Risk, Allowance, and Finance teams to ensure consistency and appropriateness of the team’s various processes

Overview of the Role
As a Data Scientist on the Consumer Risk team, your main responsibilities will include:
• Managing a portfolio of data intensive operational processes that span multiple complex technologies and infrastructures 
• Building and running operational processes, across large complex multi-sourced data, often on a wide range of quantitative models using applications and coding based solutions  
• Managing and monitoring controls across model execution and / or the sourcing and provisioning of complex data for multiple end-users 
• Managing cycle-over-cycle executions and shaping the strategic direction of operations in a highly regulated environment
• Interacting with multiple stakeholders to drive consistent on-time delivery of well-considered and thorough solutions, often with short delivery times
• Leveraging technical skills to improve, enhance, and automate existing processes 
• Providing regular updates to various stakeholders and senior leaders

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

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