Applied Artificial Intelligence & Machine Learning - Model Governance - Vice President

With the increase in accessibility and usage of Machine Learning models, the standards that these models must adhere to are evolving at a rapid pace. The ML Model Risk team helps the developers adhere to these ever-evolving standards and regulations and helps them take a risk first approach to model development.

Job summary :

As a Machine Learning Model Risk VP within our Applied AI and Machine Learning organization, you will be part of a team focused on reducing model risk. You will ensure robust model development processes, apply machine learning and data science methodologies to financial markets, and collaborate with developers to reduce model risk. You will also guide models through the review process, evaluate new ML vendor tools, and ensure compliance with evolving ML development standards. This role provides an opportunity to participate in cutting-edge research and contribute to the development of our machine learning models. You will expected to guide and lead junior data scientists

Job responsibilities:

• Participating in cutting edge research and applying machine learning and data science methodologies to financial markets and related operations
• Analyze the models through a risk-based lens and collaborate with developers to reduce model risk
• Provide feedback and collaborate with users and developers to ensure optimal development of ML models
• Develop and maintain a list of tests to validate various common techniques used to build Machine learning models
• Guiding models through the review process and enhancing and improving them as required
• Evaluate new ML vendor tools for usefulness, effectiveness and ease of deployment in CIB
• Understand the ever evolving ML development standards and processes and communicate with developers to ensure compliance

Required qualifications, capabilities and skills :

• Master’s, PhD, or equivalent degree program in mathematics, sciences, statistics, econometrics, engineering, financial engineering, computer science, or other quantitative fields
• Mastered advanced mathematics and statistics (i.e., probability theory, time series, econometrics, optimization), with core expertise in statistics and machine learning theory, techniques, and tools
• Strong written and oral communication and interpersonal skills
• Strong Stakeholder & Project Management skills
• Ability to ask incisive questions, converge on critical matters, assess materiality and escalate issues
• Basic understanding of the company's business practices and familiarity with the company's products and services

Preferred qualifications, capabilities and skills :

• Programming experience with Python; experience in MLOps would be a positive
• Experience with python programming and implementing machine learning techniques.
• Experience in Model Risk or Machine learning teams
• Experience in developing testing frameworks for ML models
• Experience in handling multiple stakeholders