Morgan Stanley - Capital Models Role - Model Risk Management Team

Department Profile

The cornerstone of Morgan Stanley's risk management philosophy is the execution of risk-adjusted returns through prudent risk-taking that protects Morgan Stanley's capital base and franchise. Risk Management protects the firm from exposure to losses resulting from market volatilities and defaults by our lending and trading counterparties.

Background on the Position

Morgan Stanley is establishing a new Model Risk Management (MRM) team within Morgan Stanley's Mumbai offices. Morgan Stanley's Global MRM team is broadly responsible for the risk management of all of the Firm's models involving model validation, risk assessment, and governance.

Morgan Stanley is seeking a strong candidate to be a member of this new team, focused on the review, validation and risk assessment of models used both in risk management and capital planning. These models will cover market risk (e.g. VaR, FRTB), new areas of credit risk (e.g. CECL) and capital planning including machine learning techniques used in CCAR for forecasting and predictive analytics.

Primary Responsibilities

- Engage in quantitative model review and risk assessment of risk and capital models.

- Follow financial markets and business trends on a frequent basis to enhance the quality of model risk management.

- Write model risk management findings in technical documents that will be presented both internally (model developers, business unit managers) as well as regulators.

- Verbally communicate results and debate issues, challenges and methodologies with internal audiences including senior management.

Qualifications :

Skills required (essential) :

- Bachelors, Masters or Doctorate degree in a technical or quantitative finance-related area.

- Exposure to and experience in financial markets, products, and businesses.

- 0-5 years of work experience in a bank or financial institution.

- Familiarity with essential quantitative techniques used in financial and econometric models.

- Familiarity with popular machine learning techniques.

- Programming skills in C, C++, R, Python, or similar programming language.

- Strong written and verbal communication skills. Must be comfortable debating issues making formal presentations.

- Desire to work in a dynamic, team-oriented environment focusing on challenging tasks mixing fundamental, quantitative and market-oriented knowledge and skills.