Investment Risk - Associate
On-siteHong Kong, Hong Kong
Job Summary
Provide independent risk oversight across investment portfolios in Asia Pacific by analyzing exposures to market, liquidity, concentration, leverage, currency, factor, and model risks. Monitor market developments and emerging risks driven by macroeconomic, geopolitical, regulatory, and market-structure changes while evaluating risk and return characteristics of strategies. Produce clear, actionable risk insights for portfolio managers, senior leaders, and risk governance forums, supporting investment reviews, risk committees, and product approvals. Develop and enhance risk metrics, dashboards, and reporting to improve transparency, leveraging data analytics, automation, and AI tools to strengthen monitoring and intelligence. Partner with colleagues across Investments, Risk, Technology, Data, Product Development, and Control functions to contribute to strategic initiatives focused on innovation and risk transformation.
Required Qualifications
- Bachelor's degree (or equivalent) in Finance, Economics, Mathematics, Engineering, Computer Science, Data Science, or a related field
- Strong analytical and quantitative skills, with ability to evaluate complex investment and portfolio risks
- Understanding of financial markets and investment management concepts
- Ability to synthesize information and communicate risk issues clearly in writing and in discussions with technical and non‐technical audiences
- Strong organizational skills with ability to manage multiple priorities in a fast‐paced environment
- Demonstrated ownership, intellectual curiosity, and attention to detail
- Ability to work independently and collaborate effectively across teams and regions
- Interest in investment risk management, portfolio analytics, market structure, and emerging technologies
Desired Qualifications
- 4+ years of relevant experience in asset management, investment risk management, portfolio analytics, research, or related fields
- Familiarity with risk analytics such as VaR, stress testing, scenario analysis, factor models, or performance attribution
- Experience with data and analytics tools such as Python, SQL, Tableau, Power BI, Alteryx, or similar technologies
- Knowledge of quantitative techniques, machine learning, or AI‐enabled analytical solutions
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