Overview of Position:
The Risk and Capital Function works with members, regulators, policy makers and other standard setting bodies to develop an industry position and leverages in-house technology to provide analysis and advice on risk, margin and regulatory capital issues in the cleared and uncleared derivatives market.  Quantitative analysis and technological efficiency are critical for ISDA both in terms of meeting contractual and business requirements, including the development of fact-based advocacy.

As part of the Capital team, the Benchmarking initiative has been developed to provide a framework that

• supports regulators’ objectives for benchmarking capital models
• makes participating in benchmarking a value proposition for ISDA members
• has developed tools and data standards that promote efficiencies for firms participating in benchmarking exercises.

As the adoption of the internal model method (IMM) for counterparty credit risk is on the rise across Europe and the UK, a framework for benchmarking its results across banks is currently being developed. Such models are complex and data-heavy in nature, which raises the question whether any machine learning/AI techniques may prove helpful in identifying outliers and interesting trends in the dataset. The candidate will therefore be tasked with compiling an overview of applicable techniques and algorithms, and to test those on the IMM benchmarking dataset, ultimately providing some recommendations on the most relevant ones for this specific context.

Duties and Responsibilities:

• The candidate must have current knowledge of machine learning/AI techniques, which they will be asked to apply to analyze result sets from a most recently completed counterparty credit risk IMM benchmarking exercise.
• The candidate will need to be comfortable processing and comparing large data sets.
• The research will be presented to senior members of the Risk and Capital team at ISDA.
• The candidate will have visibility on other projects of the Risk & Capital team and might be asked to contribute as well.
• The work will provide the Intern with genuine opportunities to acquire skills in the following areas:  Counterparty Risk, Market Risk, Data science, Report writing and presentation skills
• Respect and promote the ISDA corporate values

Qualifications

• Must be currently enrolled or freshly graduated in a Master university program in a quantitative subject (e.g. Mathematics, Quantitative Finance, Engineering)
• Must have demonstrable knowledge of machine learning/AI techniques for data analysis
• Strong data science / analysis skills
• Interested in finance and derivatives
• Excellent written and verbal communication skills
• Eagerness to tackle new projects, do self-research as well as work collaboratively with ISDA staff