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Job Description:
The
Data Operations Analystsupports the Service Asset Configuration Management (SACM) Operations team which is responsible to ensure the assets (hardware/software) required to deliver services are properly controlled, and that accurate and reliable information about those assets are available when and where it is needed. The SACM Operations team provides reporting and operational capability for managing the inventory within the CMDB; including management of the Remedy internal product catalog.
This job is responsible for developing and delivering data solutions to accomplish technology and business goals and initiatives. Key responsibilities include performing code design and delivery tasks associated with the integration, cleaning, transformation, and control of data in operational and analytical data systems. Job expectations include working with stakeholders and Product and Software Engineering teams to aid with implementing data requirements, analyzing performance, and researching and troubleshooting data problems within system engineering domains.
Responsibilities:
Required Qualifications
Desired Qualifications
Skills:
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Responsibilities include day-to-day analysis, research and resolution of varied problems including receiving 50+ calls per day. Functions include administering defined procedures, analysis and report generation, and exception processing. Coordinates the implementation of products/services and operational solutions. May assist in designing, documenting, implementing, and monitoring of new products/services. Provides guidance to associates within own unit regarding procedural, technical and/or operational changes. Incumbents have full working knowledge of operations environment acquired through experience in an operations environment and on the job training.
Minimum Required Skills:
Highly organized with strong attention to detail
Experience receiving calls throughout the business day, up to 50+ calls per day
Typing and Data entry experience
Client facing experience
Excellent time management and prioritization skills, with ability to meet deadlines under pressure
Excellent oral and written communication skills
Exceptional Customer Service experience
Experience with working independently and on teams
Experienced with Microsoft Office Suite, especially Excel
Experienced with managing multiple priorities and multi tasks
Desired Skills:
Expert level Excel experience
Working knowledge of payment investigations systems
Financial industry experience
Business, Finance, Accounting, Economics, or similar major preferred
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This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products.
Responsibilities:
Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers
Supports the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization
Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation
Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
Supports the methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk
Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes
Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches
Overview of Global Risk Analytics (GRA) and Enterprise Independent Testing (EIT)
Bank of America Merrill Lynch has an opportunity for a Quantitative Financial Analyst 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 Model Development
The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business. The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas:
Quantitative Modeling – Develop and maintain risk and capital Models and Model Systems across Retail and GWIM product lines. Models and Model Systems provide insight into many risk areas, including loan default, exposure at default (EAD), loss given default (LGD), delinquency, prepayment, balances, pricing, risk appetite, revenues and cash flows.
Quantitative Solutions Engineering – Architect, implement, maintain, improve and integrate quantitative solutions on strategic GRA platforms. Outputs include GRA libraries that perform consumer risk model calculations, analytical tools, processes and documentation. Partner in defining, adopting, and executing GRA’s technical strategy.
Risk and Capital Management Capabilities – Build best in class quantitative solutions that enable the Retail and GWIM lines of business to effectively manage risk and capital, through the application of the disciplined BAU development process that includes extensive interaction with the FLU model owners and stakeholders throughout the quantitative lifecycle.
Infrastructure – Partner in driving forward the infrastructure to support the goals of GRA through code efficiencies, and expansion of quantitative capabilities to better leverage infrastructure and computational resources.
Documentation – Deliver concise, quantitative documentation to inform stakeholders, meet policy requirements, and enable successful engagement in regulatory exams (e.g., CCAR, CECL) via automated, modularized, and standardized documentation and presentations.
The Quantitative Finance Analyst will interact with a wide variety of stakeholders including risk managers, model developers, operations, technology, finance, and capital.
The main responsibilities will involve:
Software development: implement, maintain, improve and integrate quantitative solutions on strategic GRA platforms. Development takes place almost entirely in Python, with some C++ for high performance components.
Maintain code quality through best practices, unit testing and code quality automation.
Understand the whole product, its modules, and the interrelationship between them, while being an expert in the assigned component or module.
Possess advanced domain knowledge and show great customer focus. Leverage skills in methodologies and build, release, and deployment processes.
Partner in defining, adopting, and executing GRA’s technical strategy.
Identify and apply new software development techniques to support enhanced granularity of risk management capabilities. Employ elevated intellectual curiosity and an acute sense of innovation.
Elevated intellectual curiosity with acute sense of innovation to identify and apply new statistical and econometric techniques to support enhanced granularity of risk management capabilities
Articulating the overall holistic picture of model performance, with clear conclusions regarding accuracy and remediation areas as required
Minimum Education Requirement:Master’s degree in related field or equivalent work experience
Required Qualifications:Successful candidates will have a minimum 5 years relevant experience and will possess the following skills:
Strong Python software development skills
Familiar with software design principles: separation of concerns, single responsibility, DRY, etc.
Understanding of algorithms and data structures
Experience with Linux operating system and command line tools
Experience with version control systems, i.e., Git
Knowledge of SQL
Strong communication skills and ability to effectively communicate quantitative topics to technical and non-technical audiences
Strong team player able to seamlessly transition between contributing individually and collaborating on team projects; Understands that individual actions may require input from manager or peers; Knows when to include others
Experience implementing models into various production environments
Demonstrated leadership skills; Ability to exert broad influence among peers
Strategic thinker that can understand complex business challenges and potential solutions
Sees the broader picture and is able to identify new methods for doing things
Ability to work in a large, complex organization, and influence various stakeholders and partners
Ability to work in a highly controlled and audited environment
Exceptional programming skills in high performance python
Exceptional Terabyte-scale Spark programming and optimization
Desired Qualifications:The ideal candidate will possess the following skills and experience:
GRA Core Platform (GCP) experience
Familiar with systems architecture concepts: service based, layered, microservices, scalability patterns
Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow
Experience creating, optimizing, and debugging software solutions deployed into distributed computing environments (experience with Spark is a plus)
Familiar with use of vectorization and data locality concepts to optimize software efficiency
Experience with high performance Python libraries, i.e., Numpy, Numba, Cython
Experience with LaTeX
Familiarity with SDLC tools: unit testing libraries, Jira, Jenkins
Consumer Financial Product Industry experience
Skills:
Critical Thinking
Quantitative Development
Risk Analytics
Risk Modeling
Technical Documentation
Adaptability
Collaboration
Problem Solving
Risk Management
Test Engineering
Data Modeling
Data and Trend Analysis
Process Performance Measurement
Research
Written Communications
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Job Description:
Job Description:
Quantitative engineers in Global Risk are responsible for designing and implementing common, reusable, and scalable software components. These components enable GRM’s data and analytical capabilities. These components can be domain independent (e.g., generic data quality tools over trillions of rows of data) or domain specific (e.g., classification models for surveillance or testing framework for Global Markets processes). Quantitative engineers work with modelers, risk managers, and technologists to understand the current state and design the future state of data and analytics. Quantitative engineers have a combination of software engineering, big data, and modeling skills and the ability to work across the entire spectrum of a big data stack – from data to logic to model to UI to UX.
Responsibilities:
Applying quantitative methods to develop capabilities that meet line of business, risk management and regulatory requirements
Understanding financial data: schemas, flow, size, data issues, data controls, etc.
Building performant big data pipelines
Use programming skills and knowledge of software development lifecycle principles to deliver high quality code for model and testing processes
Collaborate with key stakeholders across the Bank to understand modeling and testing business processes and requirements
Think outside the box of current industry standards to develop innovative approaches
Maintaining and continuously enhancing capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks
Source and evaluate data required for modeling and testing
Design and develop and implement models and tests
Produce clear, concise and repeatable technical documentation models and testsfor internal and regulatory purposes
Candidates should meet all or a subset of the following technical skills:-
Software engineering: modular code, software lifecycle processes, unit testing, regression testing
Big data: distributed computing paradigms (e.g., mapreduce, dataframes, etc), optimizing distributed software
Modeling / quantitative: basic modeling techniques (regression, classification, clustering, etc)
Minimum Education Requirement:
Bachelor’s degree in Computer Science, a closely related field, or a degree from a program where software engineering was a key focus or equivalent work experience
Qualifications:
At least 2 years of relevant experience in software engineering in Quantitative Finance or other industries
Strong Programming skills (e.g., Python) and solid understanding of Software Development Life cycle principles
Candidates should have at least one of these following skills and preferably have at least two of these skills:-
Strong analytical and problem-solving skills
Experience applying quantitative methods such as modelling, data analytics, machine learning, and statistics to develop business solutions
Experience with large scale data sets with structured or unstructured data
Experience in building user facing applications over large amounts of data using technologies like React, Angular, JavaScript etc.
Experience implementing process improvements and automation
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This job is responsible for conducting quantitative analytics and complex modeling projects for specific business units or risk types. Key responsibilities include leading the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.
Responsibilities:
Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers
Leads the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization
Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on modeldevelopment/validation
Maintains and provides oversight of model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
Leads and provides methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches ofdevelopment/validationprojects and identify areas of potential risk
Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes
Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches
Overview of Global Risk Analytics:
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 these activities.
Overview of Global Risk Analytics Team:
The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business. The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas:
Overview of the Role:
The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business.
The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas:
Quantitative Modeling – Develop and maintain risk and capital Models and Model Systems across Retail and GWIM product lines. Models and Model Systems provide insight into many risk areas, including valuation modeling of residential real estate, loan default, exposure at default (EAD), loss given default (LGD), delinquency, prepayment, balances, pricing, risk appetite, revenues and cash flows.
Quantitative Development – Architect, implement, maintain, improve and integrate quantitative solutions on strategic GRA platforms. Outputs include GRA libraries that perform consumer risk model calculations, analytical tools, processes and documentation. Partner in defining, adopting, and executing GRA’s technical strategy.
Risk and Capital Management Capabilities – Build best in class quantitative solutions that enable the Retail and GWIM lines of business to effectively manage risk and capital, through the application of the disciplined BAU development process that includes extensive interaction with the FLU model owners and stakeholders throughout the quantitative lifecycle.
Infrastructure – Partner in driving forward the infrastructure to support the goals of GRA through code efficiencies, and expansion of quantitative capabilities to better leverage infrastructure and computational resources.
Documentation – Deliver concise, quantitative documentation to inform stakeholders, meet policy requirements, and enable successful engagement in regulatory exams (e.g., CCAR, CECL) via automated, modularized, and standardized documentation and presentations.
Qualified candidates must be able to work independently to provide sound economic reasoning, statistical analysis and deliver high quality modeling insights as well as modeling documentation. The ideal candidate is self-directed, collaborative, analytical, and proactive in execution and problem resolution. Specific tasks include:
Set priorities related to quantitative modeling in line with the bank’s overall strategy and prioritization.
Develop and design best in class models to satisfy stakeholder requirements.
Identifies continuous improvement through reviews and ongoing monitoring of models, and effective challenges on model development and validation.
Work closely with Technology Team to support model execution.
Collaboration with Enterprise Model Risk Management to support model validations, and quickly and efficiently resolve outstanding issues.
Create sophisticated, value-added analytic systems that support business operations, risk management, operational excellence, regulatory compliance, and research.
Support business units and acting as a subject matter expert on specified quantitative modeling techniques, as well as oversee model performance, model risk and model governance on critical model portfolios.
Work closely with model stakeholders and senior management with regard to communication of submission and validation outcomes.
Required Qualifications:Successful candidates will have a Master or PhD degree in Mathematics, Economics, Statistics, or similar discipline, and a minimum of 5 years relevant experience in statistics, data science, econometrics, and other quantitative analysis.
Successful candidates will possess the following skills:
First-hand experience in large data analysis, statistical model estimation, implementation, and testing
Ability to work in a large, complex organization, and influence various stakeholders and partners
Strong team player able to seamlessly transition between contributing individually and collaborating on team projects; Understands that individual actions may require input from manager or peers; Knows when to include others.
Strong communication skills and ability to effectively communicate quantitative topics to technical and non-technical audiences.
Strong programming skills in SQL, Python, R
Strong analytical and problem-solving skills
Strong ownership and accountability for delivering high quality work, able to prioritize effectively, adapt, and meet strict deadlines
Strong written, verbal, presentation creation and delivery skills
Desired Qualifications:The ideal candidate will possess the following skills and experience:
Knowledge of financial services industry, consumer credit and products, real estate data and market, and related regulations
Experience with HDFS, HIVE, and Spark
Experience with CCAR and CECL
Ability to apply CI/CD tools (e.g.,, Git, JIRA, Confluence, Pytest, Jenkins, and SonarQube) in model development process
Experience implementing process improvements and automation.
Managerial experience
Data visualizations in Tableau
Skills:
Critical Thinking
Quantitative Development
Risk Analytics
Risk Modeling
Technical Documentation
Adaptability
Collaboration
Problem Solving
Risk Management
Test Engineering
Data Modeling
Data and Trend Analysis
Process Performance Measurement
Research
Written Communications
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This job is responsible for driving and executing on internal control discipline and operational excellence within a Line of Business (LOB) or Enterprise Control Function (ECF). Key responsibilities include providing analytical and administrative support for LOB or ECF specific processes and tools to drive adherence to enterprise-wide standards. Job expectations include monitoring and testing controls and implementing quality assurance and quality control processes within the LOB or ECF by evaluating data to help identify, track, and report issues and control improvements for remediation.
Responsibilities:
Skills:
Key Responsibilities:
Required Skills:
Desired Skills:
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Job Description:
Job Description:
Responsibilities:
Review loan documentation in compliance with bank policies and procedures.
Independently research and discuss loan details with line partners, demonstrating confidence and subject matter expertise.
Loan bookings of varying complexity and ensure world-class accuracy.
Maintain responsibility for input loan bookings into SOR (System of Record) and minimize controllable rework.
Collaborate with Client Managers, Credit and Portfolio Officers, agency management, legal counsel, and internal support units to ensure seamless transaction execution
Minimum Qualifications:
Minimum 2 years’ experience with commercial and/or consumer loan documentation review.
Familiarity with GFS and Loan IQ systems.
Strong written and verbal communication skills.
Excellent organizational and multitasking abilities.
Detail-oriented with a high level of accuracy.
Proficient in Microsoft Word and Excel.
Ability to work effectively in a team environment, including with remote teammates.
Skills:
Attention to Detail
Legal Structures and Legal Forms
Oral Communications
Prioritization
Recording/Organizing Information
Written Communications
Data Collection and Entry
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Job Description:
Job Description:
The
Data Operations Analystsupports the Service Asset Configuration Management (SACM) Operations team which is responsible to ensure the assets (hardware/software) required to deliver services are properly controlled, and that accurate and reliable information about those assets are available when and where it is needed. The SACM Operations team provides reporting and operational capability for managing the inventory within the CMDB; including management of the Remedy internal product catalog.
This job is responsible for developing and delivering data solutions to accomplish technology and business goals and initiatives. Key responsibilities include performing code design and delivery tasks associated with the integration, cleaning, transformation, and control of data in operational and analytical data systems. Job expectations include working with stakeholders and Product and Software Engineering teams to aid with implementing data requirements, analyzing performance, and researching and troubleshooting data problems within system engineering domains.
Responsibilities:
Required Qualifications
Desired Qualifications
Skills:
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