Sr. Consultant- Quant- Model Risk Management

1 month ago

Pune, India Solytics Partners Full time

Company Profile:


Solytics Partners provides Consulting and Solutions to Banking, Capital Markets, Asset Management, and Insurance firms.

We leverage combination of deep domain knowledge, advanced analytics and technology to provide accelerated and efficient services and next generation solutions. Our team of senior consultants comes with significant global experience in key markets and advanced degrees in STEM. Our regulatory compliant solutions and services enable leading financial institutions and corporations to create and sustain competitive advantage.


Role Overview:


This role is aligned with the Model Risk Management team and involves analysis review, validation and quantitative Risk models like IFRS9, Basel models, stress testing, ICAAP, ILAAP, IRRBB. Should have worked for US FED, PRA, Bank of England, MAS or HKMA based regulators.


Role and Responsibilities:

  • Develop and validate regulatory credit risk models.
  • Adhere to conceptual soundness, standards of modelling methodology, model limitations, data quality, model monitoring.
  • Responsible for Risk Management deliverables for specific class of models and represent model risk team.
  • Document MRM analysis and finding in model documents which can be presented to internal and external teams like model developers, business managers and regulators.
  • Develop and maintain model risk documentation, including Model risk management policy and procedures, Model risk assessment reports, Model inventory.
  • Communicating with senior stakeholders on issues, challenges, and methodologies.
  • Act as a point of contact from Model Risk Management team in interactions with internal stakeholders or external stakeholders - regulatory or audit agencies as required.



Key Skills:


  • 4 to 8 years of experience in validation of quantitative risk models.
  • Work experience in a Quantitative Risk role like Model Validation in a bank or financial institution.
  • Strong understanding of Regression Analysis, Factor Modelling and Statistical Testing as a part of Model Validation.
  • Hands-on experience in Python, R or similar statistical programming language.
  • Understanding of macroeconomics, markets, financial instruments, and their interrelationships
  • Experience in managing model validation projects and consulting engagements in model validation with peer banks

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