Vacancy expired!
Director of Risk Analytics & Systems (“RAS”) will report directly to the Head of Risk Management and will establish and lead a team of analysts and quants to develop Risk Management analytics, systems, and quantitative models. The RAS team is tasked with the integration of third-party risk systems; the development and reporting of all investment risk metrics and measures; and the development of all quantitative and analytical risk models in support of all other Risk Management functions.
Essential activities: Integrating risk systems, risk reporting, data management, rapid analysis on data issues, advanced data manipulation, and modeling skills, knowledge of SQL, Python, VBA, C, etc. Technical Skills:- Data management
- Python/ C/VBA programming experience
- PowerBI (or some equivalent BI tool such as Tableau)
- MSCI Risk Manager, MSCI BarraOne, FactSet, Bloomberg working knowledge is desirable
- SQL querying
- ETL (Extract Transform load) exposure
- Python data science experience
- Understanding of multi-asset products is desirable
- Comprehension of quantitative risk modeling is desirable
- Exposure to the investment management lifecycle is desirable
- Advanced Degree in quantitative finance, or another related field, CFA and/or FRM, etc.
- Minimum five-years’ experience in managing a team of technical and quantitative analysts.
- 5-10 years’ experience in data analytics, or related quantitative fields as it applies to risk management or middle office investment support of analytics and systems.
- Quantitative financial modeling in market, investment, or credit risk is desirable but not necessary, with some knowledge of factor risk analysis.
- Experience in working with/understanding the various lifecycles of data management, extraction, loading, transformation to its applications in risk measurement and reporting.
- Extensive knowledge in composing SQL queries, data modeling experience working with large structured and financial data sets.
- Ability to tactically understand results in context (i.e. when does something look wrong), and strategically how to resolve those issues via data quality and rule-based analysis.
Vacancy expired!