New opportunity

Senior Operation Manager

Bank of America · Mumbai, India

About this role

The Senior Operation Manager role within the Analytics, Research and Quants (ARQ) division at Bank of America is responsible for supporting quantitative analytics, model governance, and ongoing model performance monitoring across a portfolio of models for specific business units or risk types. The role requires a strong background in data science, statistics, and risk management to influence stakeholders and ensure operational excellence. Responsibilities: Develop new models, analytic processes, or systems approaches; create technical documentation; collaborate with Technology staff to design systems for model execution; manage the Intracycle Monitoring (ICM) process; oversee model performance reporting; maintain model inventories; assess model health and performance trends; and ensure compliance with Model Risk Management (MRM) requirements. Stakeholder management duties include serving as a primary point of contact for Model Development Executives, Model Developers, Model Owners, and MRM, while communicating model performance outcomes, monitoring insights, and remediation progress. Requirements: Master’s degree in a related field, Quantitative studies, or Data Science; 10+ years of experience; proficiency in Python, SQL, and Latex; and strong skills in risk analytics, data modeling, and process performance measurement. Desired skills include research and written communication. The position is based in Mumbai, India, with work timings from 12 pm to 9 pm IST.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Critical ThinkingQuantitative DevelopmentRisk Analytics & ModelingTechnical DocumentationAdaptabilityCollaborationProblem SolvingRisk ManagementData ModelingData and Trend AnalysisProcess performance measurementPythonSQLLatexResearchWritten Communications

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Model Governance and Compliance
Ensuring all model development and monitoring activities strictly adhere to Model Risk Management (MRM) requirements. This requires maintaining accurate model inventories and ensuring all processes are documented to meet regulatory and internal audit standards.
Stakeholder Communication and Influence
Acting as the primary liaison between technical teams (Model Developers) and business stakeholders (Model Owners, MRM). You must translate complex model performance outcomes and governance insights into clear, actionable information for non-technical leadership.
Operational Model Monitoring
Managing the Intracycle Monitoring (ICM) process and overseeing ongoing performance reporting. This involves identifying health trends, detecting performance degradation, and coordinating remediation efforts to maintain operational excellence.
Technical Systems Design and Development
Collaborating with Technology staff to design and implement robust systems for model execution. This requires applying data science and quantitative expertise to build scalable analytic processes and systems approaches.

Likely challenges to think through

Balancing Technical Rigor with Regulatory Speed
The role requires maintaining high standards of model health and compliance while supporting fast-paced business needs. Prepare by demonstrating how you have managed competing priorities in a highly regulated environment without sacrificing documentation quality or governance integrity.
Cross-Functional Influence Without Direct Authority
You must influence Model Development Executives and Model Owners to remediate issues. Prepare by highlighting your experience in building consensus across diverse teams and your ability to use data-driven insights to persuade stakeholders to prioritize model health.
Managing Complex Model Portfolios
Overseeing a portfolio of models across different business units introduces significant complexity in performance measurement. Prepare by discussing your methodology for prioritizing monitoring efforts and your approach to standardizing reporting across varied risk types.

Practice your answers

Technical practice

Model Risk Management (MRM) Compliance

What to coverExplain how you ensure model development and monitoring processes align with strict MRM requirements. Focus on the importance of maintaining comprehensive model inventories, audit trails, and documentation to satisfy regulatory and internal governance standards.

Intracycle Monitoring (ICM) Frameworks

What to coverDescribe your experience managing Intracycle Monitoring processes. Discuss how you identify performance drifts or anomalies between scheduled model validations and the steps taken to remediate these issues.

Quantitative Model Performance Reporting

What to coverHow do you translate complex quantitative model outputs into actionable performance reports for non-technical stakeholders? Emphasize the use of clear metrics, trend analysis, and visual storytelling to communicate model health.

Data Modeling and SQL Proficiency

What to coverDiscuss a scenario where you had to optimize a SQL query or data model to improve the efficiency of a quantitative process. Focus on how you handle large datasets while ensuring data integrity and accuracy for risk analytics.

Python for Quantitative Development

What to coverProvide an example of using Python to automate a manual analytical process or to build a new model component. Highlight your ability to write clean, maintainable code that integrates with existing production systems.

Technical Documentation Standards

What to coverWhy is rigorous technical documentation critical in a highly regulated banking environment? Explain your approach to using tools like Latex to create professional, reproducible documentation for model methodologies and performance results.

Risk Analytics and Trend Analysis

What to coverDescribe a time you identified a negative trend in model performance. Explain the statistical methods you used to diagnose the root cause and the subsequent steps taken to mitigate the associated risk.

System Design for Model Execution

What to coverHow do you collaborate with Technology teams to transition a model from development to a production environment? Discuss the trade-offs between model complexity and operational feasibility in a high-performance banking system.

Behavioural practice

Influencing Senior Stakeholders

What to coverDescribe a situation where you had to influence a Model Development Executive or Model Owner regarding a governance concern. Focus on how you used data-driven insights to reach a consensus while maintaining professional relationships.

Managing Cross-Functional Collaboration

What to coverGive an example of a project where you had to coordinate between Model Developers, Technology staff, and Risk Management. How did you manage conflicting priorities to ensure the project met its deadlines?

Adaptability in a Dynamic Environment

What to coverTell me about a time when regulatory requirements or business priorities shifted suddenly. How did you adapt your team's workflow or model monitoring strategy to remain compliant and effective?

Problem Solving Under Pressure

What to coverDescribe a high-stakes situation where a model failed to perform as expected. Walk through your process for identifying the issue, communicating the impact to stakeholders, and implementing a remediation plan.

Sharpen your knowledge

Topics to prepare

Model Risk Management (MRM) Frameworks
Review regulatory standards and internal governance requirements for model risk. Exercise: Draft a summary of how a model's lifecycle, from development to retirement, aligns with standard MRM compliance protocols. This is critical for the role's responsibility in ensuring model health and regulatory adherence.
Intracycle Monitoring (ICM) and Performance Reporting
Understand the methodologies for monitoring model performance between scheduled validation cycles. Exercise: Design a hypothetical monitoring dashboard structure that tracks key performance indicators (KPIs) and identifies drift in model health. This directly supports the requirement to manage ICM processes and report performance outcomes.

Skills to revise

Technical Documentation for Quantitative Models
Practice creating clear, structured documentation for complex analytical processes using Latex. Exercise: Write a technical document explaining a standard risk model's logic, assumptions, and limitations. This is essential for maintaining model inventories and communicating with Model Risk Management.
SQL and Python for Data Modeling and Analysis
Refine skills in querying large datasets and performing statistical analysis. Exercise: Write a SQL query to extract model performance data and a Python script to calculate trend metrics for a specific risk type. This is required for assessing model health and managing performance reporting.
Stakeholder Communication for Technical Insights
Develop the ability to translate complex quantitative findings into actionable business insights. Exercise: Prepare a brief presentation or memo summarizing a model's performance degradation and proposed remediation steps for a non-technical stakeholder. This is vital for the role's primary contact duties with Model Developers and Executives.

Your preparation checklist

Prepare a technical case study on model governance
Develop a concise walkthrough of a past project where you managed the lifecycle of a quantitative model, specifically highlighting your experience with model performance monitoring, inventory maintenance, and ensuring compliance with risk management frameworks.
Demonstrate proficiency in Python, SQL, and Latex
Prepare code snippets or documentation samples that showcase your ability to perform data analysis in Python, extract complex datasets using SQL, and produce professional-grade technical documentation using Latex.
Formulate a stakeholder communication strategy
Draft a plan for how you would communicate complex model performance outcomes and governance concerns to diverse stakeholders, such as Model Development Executives and Model Risk Management teams, ensuring the information remains actionable.
Review Model Risk Management (MRM) standards
Familiarize yourself with standard regulatory requirements for model risk management to discuss how you have previously ensured operational excellence and compliance within a highly regulated financial environment.
Prepare targeted questions for the interviewers
Draft thoughtful questions regarding the current challenges in the ARQ division's model inventory, the specific systems used for Intracycle Monitoring (ICM), and how the team balances technical development with ongoing performance reporting.

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