New opportunity

Quantitative Analytics Market Risk Modeler

Barclays · Mumbai, India

About this role

Join the Barclays Quantitative Analytics Team as a Quantitative Analytics Market Risk Modeler. You will be responsible for designing, developing, implementing, and supporting mathematical, statistical, and machine learning models used in business decision-making. You will work in a global quant team, collaborating with regulators and utilizing cutting-edge technology to develop best-in-class market risk models. Responsibilities include designing analytics solutions for complex business problems, collaborating with technology teams on data and environment dependencies, and ensuring high-performing, well-documented models that conform to Barclays Enterprise Risk Management Policies. As an Assistant Vice President, you will lead teams or guide collaborative assignments, influence decision-making, and mitigate risk. Required qualifications include hands-on experience in model development or validation in areas such as FRTB, VaR, Stress Testing, or CVA, and proficiency in Python, R, or C++. Desired qualifications include an advanced technical degree (Master's or PhD) in fields like Statistics, Mathematics, Physics, or Financial Engineering, and relevant certifications such as GARP-FRM, PRM, or CQF. The role is based in Mumbai and operates under a hybrid working model.

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

Skills for this role

PythonRC++Market Risk ModelingFRTB IMA & SAVaRIRC / DRCExpected ShortfallEconomic Risk CapitalCCARStress TestingCVAExposure & Risk Factor ModellingBack-testingNumerical AnalysisMachine LearningStatistical ModelingModel DevelopmentModel Validation

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

End-to-End Model Lifecycle Management
The role requires designing, developing, implementing, and supporting market risk models. You must demonstrate proficiency in the full lifecycle, from initial mathematical design and coding in Python, R, or C++ to operationalizing models and providing ongoing support for their effectiveness.
Regulatory Compliance and Governance
You must ensure all models conform to Barclays Enterprise Risk Management Policies, specifically the Model Risk Policy. This involves documenting models comprehensively to satisfy independent validation teams and global regulators, ensuring all activities occur within a strictly defined control environment.
Technical Leadership and Influence
As an Assistant Vice President, you are expected to lead collaborative assignments or teams. You must influence decision-making, advise stakeholders on complex issues, and guide team members through structured projects while maintaining high standards of operational effectiveness.

Likely challenges to think through

Balancing Model Complexity with Operational Stability
Developing sophisticated machine learning or statistical models that are also high-performing and stable in production is a core difficulty. Prepare by discussing how you have translated complex mathematical concepts into robust, well-tested software that integrates seamlessly with existing technology infrastructure.
Navigating Cross-Functional Dependencies
The role requires heavy collaboration with technology teams for data and environment dependencies. You may face challenges in aligning technical requirements with business needs. Prepare by highlighting your experience in managing cross-functional communication and resolving technical bottlenecks to meet project timelines.
Communicating Complex Risk Information
You will be required to communicate sensitive or complex information to diverse audiences, including business users and regulators. Prepare by practicing how you explain technical model limitations or risk findings to non-technical stakeholders to influence their decision-making process.

Practice your answers

Technical practice

FRTB Implementation Challenges

What to coverDiscuss the key differences between the Internal Models Approach (IMA) and Standardized Approach (SA) under FRTB. Explain how you would handle the transition from VaR to Expected Shortfall (ES) in terms of data requirements and computational complexity.

Model Validation and Control Frameworks

What to coverDescribe your approach to ensuring a model conforms to strict Enterprise Risk Management policies. Explain how you document model limitations and ensure independent validation teams can reproduce your results.

Stress Testing and Economic Capital

What to coverHow do you design stress testing scenarios that capture tail risk effectively? Discuss the relationship between CCAR requirements and internal Economic Risk Capital calculations.

CVA and Exposure Modeling

What to coverExplain the methodology for modeling Credit Valuation Adjustment (CVA) and the importance of capturing wrong-way risk. How do you handle dependencies between market risk factors and counterparty credit quality?

Numerical Analysis in Risk Modeling

What to coverWhen implementing complex risk models, how do you balance computational efficiency with numerical stability? Discuss your experience with optimization techniques or Monte Carlo simulation improvements in Python or C++.

Machine Learning in Market Risk

What to coverProvide an example of how you have applied machine learning to a market risk problem, such as risk factor proxying or volatility surface modeling. Address how you ensure model interpretability and regulatory compliance for these non-linear models.

Back-testing Methodologies

What to coverWhat are the standard procedures for back-testing VaR models, and how do you interpret exceptions? Suggest how you would refine a model that consistently fails back-testing at a specific confidence level.

Data Dependency and Operationalization

What to coverDescribe a situation where you had to collaborate with technology teams to resolve data quality or environment issues. How do you ensure that the production implementation of a model remains consistent with the research prototype?

Behavioural practice

Influencing Stakeholders on Complex Issues

What to coverDescribe a time you had to communicate a complex or sensitive model limitation to non-technical stakeholders. How did you influence their decision-making while maintaining transparency about the associated risks?

Leading Collaborative Assignments

What to coverProvide an example of leading a cross-functional project where you had to coordinate with different areas of specialization. How did you ensure the team stayed aligned and met the project objectives?

Managing Risk and Strengthening Controls

What to coverTell me about a time you identified a potential risk in an existing process or model. What steps did you take to mitigate that risk and improve the control environment?

Coaching and Developing Others

What to coverAs an AVP, how do you approach setting objectives and coaching team members to deliver high-quality work? Share an example of how you helped a colleague improve their performance or technical skills.

Sharpen your knowledge

Topics to prepare

Fundamental Review of the Trading Book (FRTB)
Review the regulatory requirements for FRTB, specifically the Internal Models Approach (IMA) and Standardized Approach (SA). Exercise: Draft a summary comparing the capital charge calculation differences between the IMA and SA frameworks to prepare for discussions on model implementation and regulatory compliance.
Market Risk Model Lifecycle and Governance
Understand the end-to-end model development lifecycle, including design, implementation, documentation, and independent validation. Exercise: Create a mock model documentation outline that addresses the Barclays Enterprise Risk Management Policy requirements, focusing on how to justify model assumptions and performance metrics to validation teams.
Stress Testing and Economic Risk Capital
Study methodologies for stress testing and the calculation of Economic Risk Capital. Exercise: Design a hypothetical stress testing scenario for a specific asset class (e.g., credit or interest rates) and outline the statistical steps required to estimate potential losses under that scenario.

Skills to revise

Quantitative Programming (Python, R, or C++)
Demonstrate proficiency in implementing mathematical models in a production-ready environment. Exercise: Write a script in Python or C++ that performs back-testing on a Value-at-Risk (VaR) model, comparing predicted losses against historical market data to assess model accuracy.
Numerical Analysis and Statistical Modeling
Apply advanced mathematical techniques to solve complex financial problems. Exercise: Implement a numerical method (such as Monte Carlo simulation or finite difference methods) to price a derivative or calculate a risk factor, ensuring the code is optimized for performance and stability.
Machine Learning for Risk Analytics
Utilize machine learning to enhance risk modeling and business decision-making. Exercise: Develop a prototype model using a machine learning library to identify non-linear relationships in risk factor data, documenting how the model output influences risk mitigation strategies.

Your preparation checklist

Prepare a technical walkthrough of a market risk model
Select a past project involving FRTB, VaR, or Stress Testing. Prepare to explain your methodology, the mathematical assumptions made, the data sources used, and how you validated the model's performance against regulatory standards.
Demonstrate proficiency in Python, R, or C++
Prepare code samples or a GitHub repository that showcases your ability to implement numerical analysis or statistical models. Focus on clean, well-documented code that reflects production-level standards and agile development practices.
Review Barclays Enterprise Risk Management Policies
Familiarize yourself with the concept of Model Risk Management. Be ready to discuss how you ensure model documentation is comprehensive and how you maintain compliance within a strictly controlled banking environment.
Develop examples of leadership and influence
As an AVP, you will lead or guide collaborative assignments. Prepare specific examples of how you have influenced stakeholders, mentored junior team members, or resolved complex technical disagreements within a cross-functional team.
Formulate questions regarding the model lifecycle
Prepare thoughtful questions for the interviewers about their current model development framework, how they collaborate with technology teams for data dependencies, and how the team balances innovation with regulatory requirements.

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