Senior Data Scientist

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 (GRA) and Enterprise Independent Testing (EIT)
Bank of America Merrill Lynch has an opportunity for a Senior Data Scientist within our Global Risk Analytics (GRA) function. Global Risk Analytics (GRA) and Enterprise Independent Testing (EIT) are sub-lines of business within Global Risk Management (GRM). Collectively, they are responsible for developing a consistent and coherent set of models, analytical tools, and tests for effective risk and capital measurement, management and reporting across Bank of America. GRA and EIT partner with the Lines of Business and Enterprise functions to ensure the capabilities it builds address both internal and regulatory requirements, and are responsive to the changing nature of portfolios, economic conditions, and emerging risks. In executing its activities, GRA and EIT drive innovation, process improvement and automation.   

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 Senior 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
• Contributing to implementation of new models along new model life cycle
• Interacting with multiple stakeholders to drive consistent on-time delivery of well-considered and thorough solutions, often with short delivery times 
• Providing regular updates to various stakeholders and senior leaders

Required Skills
• Graduate degree in a quantitative discipline
• 7+ years of experience in model development, model validation, statistical work, data analytics or quantitative research
• Intermediate to advanced experience with data structures, relational databases and SQL
• Intermediate to advanced programming skills in Python, R, or SAS
• Experience building data architecture that is optimized for large dataset retrieval, analysis, storage, cleansing, and transformation
• Additional experience with Hadoop HDFS, Hive, Impala preferred

Ability to manage and deliver:
• Cycle-over-cycle operational processes across data provisioning, and model performance monitoring
• Close collaboration with change agents driving operational excellence through strategic change
• Process execution while complying with various policies and regulations
• Code development and programming with tools such as Python, PySpark, SQL, Hadoop, Hive to develop new capabilities that meet line of business requirements

Strong operations management skills and techniques, including:
• Expert in the management of the full project life cycle – from inception to full technical and process implementation
• Expert in operating in a business and technical environment

Demonstrated personal qualities including:
• Strong understanding of process controls and safeguards
• Confident self-starter
• Quick learner and intellectually curious
• Strong communication skills, both oral and written
• Strong team player, able to lead and follow
• Strong analytical and problem-solving skills
• Strong influencing skills

Desired Skills
• Experience working with and or delivering complex data structures and processes
• Experience working for a financial institution; knowledge of retail banking products, services, processes, and systems
• Experience with data analytics / software development lifecycle tools (i.e. Alteryx, Tableau, Horizon / JIRA, etc.)
• Experience with Linux/Unix and shell scripting
• Ability to deliver large scale projects involving changes to analytical processes, quantitative models, complex technology platforms, and analytical tools

Job Description:
Responsible for enabling analysis, modeling, and optimization through producing information products. Actively involved in the research and development efforts. Primary requirement is not related to traditional programming or systems analysis skills but to the ability to create sophisticated, value-added analytic systems that support revenue generation, risk management, operational efficiency, regulatory compliance, portfolio management, and research. These systems must overcome issues of complex data (e.g., VLDB, multi-structured, big data, etc.) as well as deployment of advanced techniques (e.g., machine learning, text mining, statistical analysis, etc.) to deliver insights. Responsible for adoption of enterprise information products through clearly communicating how enterprise information products answer material banking questions leading to decisions and actions. This role often possesses an advanced degree in hard science or another heavy quantitative business or social discipline. Able to lead or work independently on complex projects and influence strategic direction.

Skills:

• Artificial Intelligence/Machine Learning
• Business Acumen
• Presentation Skills
• Project Management
• Technical Documentation
• Candidate Screening
• Data Visualization
• Policies, Procedures, and Guidelines Management
• Risk Management
• Written Communications
• Adaptability
• Career Path Design
• Consulting
• Performance Management
• Workforce Diversity Management

Shift:

Hours Per Week: