Vice President ML Model Validation
About the Role :
The VP level position requires the successful candidate to :
1. Quantitatively evaluate complex ML models
2. Build benchmark ML models with better performance / over specification tradeoffs.
- Given the firm's focus on developing Machine Learning models, a significant portion of the successful candidate's time is likely to be spent reviewing such models.
- Such models are currently used to detect fraud, allocate credit, improve marketing efforts, and optimize order routing in markets.
Core responsibilities :
- Evaluate the risk posed by ML models, and suggest ways to mitigate such risks.
- Evaluate conceptual soundness of ML model specification; reasonableness of assumptions; reliability of inputs; completeness of testing performed; correctness of implementation; and suitability/ comprehensiveness of performance metrics and risk measures.
- Ensure that the models are explainable and correctly specified.
- Liaise with Finance and Risk professionals to monitor usage and performance of models.
- Evaluate market conditions under which a given model is likely to break down.
- Cogently document findings.
- Perform, present, and publish research on ML issues facing the bank.
Desirable skills, experience, and qualifications :
- A Ph.D. or master's degree in a quantitative field such as Finance, Economics, Math, Physics or Engineering is required.
- 8+ years of experience.
- The candidate is expected to have a good understanding of machine learning models.
- Experience with large data sets and training ML models is required.
- Some understanding of statistics / econometrics is preferred.
- Thorough knowledge of at least one programming language such as R, Python, Scala, etc.
- Good communication skills. The role requires interacting with many groups across the firm, as well as producing documents for both internal and external (regulatory) consumption.
- Proven people management skills (The VP position will also involve team management responsibilities).
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