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Assistant Vice President – Retail Risk Analytics, Risk Management

RiskRiskFull TimeRemoteLeadTeam 1,001-5,000Since 1967H1B No SponsorCompany SiteLinkedIn

Location

India

Posted

2 days ago

Salary

0

Seniority

Lead

Postgraduate Degree10 yrs expEnglishHadoopPythonSQL

Job Description

Assistant Vice President – Retail Risk Analytics, Risk Management

Mashreq

• Responsible for the development, implementation, and maintenance of credit risk models and scorecards, including PD, LGD, and EAD across the retail portfolio lifecycle (acquisition, behavioral, collections). • Lead the design and enhancement of credit risk modelling frameworks, incorporating scorecards and appropriate statistical/analytical techniques to support underwriting and portfolio management decisions. • Monitor, document, and communicate the performance, assumptions, and limitations of credit risk models to stakeholders, ensuring transparency and model interpretability. • Perform model monitoring, backtesting, and periodic recalibration, ensuring models remain accurate, stable, and compliant over time. • Provide recommendations for model redevelopment or enhancement based on portfolio trends, data drift, and emerging risk patterns. • Prepare and support Basel regulatory reporting, including RWA estimation and model-related submissions aligned with internal and regulatory requirements. • Lead/support IFRS 9 ECL modelling, including staging, macroeconomic overlays, scenario-based expected credit loss estimation and stress testing including climate risk. • Deploy credit risk models into production systems / rating platforms, working closely with IT and data teams to ensure data integrity and system robustness. • Support design and implementation of credit risk strategies and decision rules (e.g., cut-offs, risk segmentation, line management) aligned with model outputs. • Perform and oversee model validation and testing activities (functional, statistical, and regulatory) prior to deployment. • Establish robust model governance practices, including documentation, audit trails, and compliance with regulatory standards. • Identify opportunities to enhance credit risk models using advanced analytics or machine learning techniques, where appropriate and justifiable. • Establish MLOps standards for model deployment, monitoring, versioning, and performance tracking in production environments. • Develop data-driven insights to monitor portfolio quality, risk trends, and early warning indicators. • Collaborate with policy, finance, and business teams to support portfolio optimization, provisioning, and capital management decisions. • Ensure timely communication of model performance, validation findings, and risk insights to senior management and committees. • Mentor junior analysts and contribute to building a strong, technically sound credit risk modelling team.

Job Requirements

  • 10–12+ years of experience in credit risk modelling within retail banking / financial services
  • Deep expertise in statistical modeling, machine learning techniques, and large-scale data analysis.
  • Strong expertise in credit risk modelling techniques, including PD, LGD, EAD, scorecards, and segmentation approaches
  • Proven experience in IFRS 9 ECL modelling and Basel frameworks
  • Strong knowledge of model lifecycle management (development, validation, deployment, monitoring)
  • Advanced technical skills in SAS, SQL, and Python/R
  • Experience in working with large datasets and data platforms (e.g., Hadoop or equivalent)
  • Strong statistical and analytical skills with ability to translate data into insights
  • Proven track record of building, deploying, and maintaining production ML models with real-time or near-real-time decisioning systems.
  • Familiarity with decision systems / rule engines is an advantage
  • Professional certifications such as FRM (Financial Risk Manager) or CFA (Chartered Financial Analyst) are a strong plus
  • Degree in Quantitative disciplines (Statistics / Mathematics / Actuarial Science / Economics)

Benefits

  • Health insurance
  • 401(k) matching
  • Flexible work hours
  • Paid time off
  • Professional development opportunities

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