VP - Model Risk - Predictive Modelling
Mumbai, India | Morgan Stanley
Job Description:117 people have viewed this job
Morgan Stanley is seeking a Vice President to join a fast-growing team in Model Risk Management, within Firm Risk and help manage a new team responsible for the review, validation, and risk assessment of models used in Wealth Management.
These high-visibility positions will be important strategic additions to a global team, focusing on the model risk oversight of Wealth Management models. These award-winning models use sophisticated Statistics and Machine Learning techniques.
Engage in quantitative model review and risk assessment of Wealth Management models.
Write model risk management findings in technical documents that will be presented both internally (model developers, business unit managers) as well as to regulators.
Verbally communicate results and debate issues, concerns and methodologies with internal audiences including senior management in Firm Risk Management
Coordinate team’s tasks and escalate issues in a timely manner.
Perform and validation of predictive models in a variety of asset classes and product categories
Participate in ongoing statistical/machine learning/deep learning research initiatives
Develop computer code in R/Python/Perl or similar languages and interface with Firm’s databases to support above mentioned initiatives
Work on Bank Model Risk projects and analyses
Possess Bachelors, Masters or Doctorate degree in a technical or quantitative-finance area
Establish and manage a team of quantitative professionals providing independent review and risk management of Wealth Management models
5+ years’ experience with quantitative modelling preferably in banks or large financial institution
Expert knowledge of predictive modeling (Linear / non-Linear regression, propensity models, Statistical / Machine Learning, etc.)
Strong programming (Python, C/C++, R etc.)
Experience with statistical/mathematical packages (R, Matlab, Mathematica, etc.)
Advanced problem solving
Have experience using popular Machine Learning or Deep Learning techniques in Python.
Have strong written and verbal communication skills; be comfortable debating issues and making formal presentations.
Have desire to work in a dynamic, team-oriented environment focusing on concerning tasks mixing fundamental, quantitative and market-oriented knowledge and skills.
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