Senior – Risk Modelling and Data Sciences

The purpose of the role encompasses the following main areas:

1. Collaborate with business, functional and risk colleagues across the company and its portfolio companies to further develop and strengthen the risk analytics and reporting framework using well known risk models.
2. Work with other heads of section of the Risk Management and Resilience department to build various @Risk models including but not limited to V@R. Run scenario analysis and stress testing to provide a quantitative view of risk at the company and its portfolio companies.
3. Data mining with a view to identifying possible traits/trends that may materialize into risk or are emerging risks using deep learning-based models. Accordingly, knowledge of Machine Learning language is essential. The Risk Management and Resilience department is keen to use historical and external data to detect and subsequently flag any emanating risks.

The overall outcome of the modeling and quantitative analysis is to enhance scientific decision making at the company's group.

Core Responsibilities

• The Risk Management and Resilience department at the company is recently set up and the position of Risk Modeling requires a candidate that is willing to work closely with the risk management team to build and tailor risk models to the business requirements and expectation of the risk team and senior management.
• Analyze enterprise risk data to gain insight into risk themes, trends, and patterns.
• Collaborate with business, functional and risk colleagues across the company's group to further develop and strengthen the risk analytics and reporting framework.
• Review external events and emerging risks to ensure they are considered in enterprise risk analytics and reporting.

Knowledge and Skill

1. The candidate should have past experiences having worked in SWF or buy side investment firms with strong global exposure covering all asset classes within private market such as direct, co-investment and PE funds.
2. Having worked on designing machine learning models for mining data, detecting anomalies/inconsistencies, neural machine translation
3. Strong risk modelling technical expertise with execution / delivery focus
4. Understanding of industry-wide Risk Modelling practices and trends
5. Experience in a Model Validation, Front Office Quant, Quantitative Risk or other relevant quantitative role
6. Ability to pull data to create machine learning models. Data could be obtained from Finance, Investment, and portfolio companies etc.
7. Work with other Risk Management team to build risk reporting for escalation to senior management and ARC. A strong understanding of Stress Testing and VAR methodologies or cross sector financial and asset pricing models.
8. Ability to build an ex-ante models based on the asset allocation framework.experience
   1. 10- 12 years of work experience in similar role having led a small team of junior staff, with a minimum of 6 years of data science/modelling/mining experience.
   2. Has proven track records of building various risk models such as V@R, IC@R, Monte Carlo Simulation model, scenario analysis models and stress testing
   3. Has the ability to continuously improve risk reporting and build various models where required by other team members.
   4. Has the ability to interpret results in simple and laymen context
   5. Has the ability to communicate/influence on complex issues
   6. Highly numerate with a high level of general intelligence