As the industry pioneer of the global ETF market, SSGA launched the first US listed ETF in 1993 (SPDR S&P 500) and has remained on the forefront of responsible innovation, evidenced by the introduction of many ground-breaking products, including first-to-market launches with gold, international real estate, international fixed income, and sector ETFs.
- Ability to conduct research and analysis independently •Conduct portfolio research and analysis to demonstrate how SPDR ETFs can be used in an asset allocation context through the use of advanced quantitative techniques developed (MATLAB and Python and/or R)
- Research asset allocation strategies using robust statistical and econometric techniques
- Service client requests through providing bespoke performance analytics
- Work with SPDR colleagues in the preparation of custom research presentations on a wide range of topics including portfolio construction using SPDR ETFs and building/testing quantitative investment strategies
- Provide clients with a variety of analytics to demonstrate the efficacy of strategies developed in-house
- Understanding of needs across the full range of SPDR ETF’s client types (intermediaries / asset managers / insurers and alternatives)
- Maintain strong focus on clients in EMEA & APAC
- Create market-leading research publications
- Write market-leading research papers using SPDR ETF as building blocks and publish them in peer-reviewed industry practitioner journals and SPDR-branded research papers in EMEA & APAC
- Completed a Bachelor's/Master's degree in a highly quantitative subject (e.g. Physics, Engineering, Computer Science or similar).
- Expert proficiency and a track record of using MATLAB and Python/or R in solving quantitative problems (these skills will be assessed as part of the interview process)
- Academic excellence
- Strong mathematical, analytical and problem-solving skills
- An interest in investment management, including asset allocation and the implementation of investment views.
- Expérience in portfolio optimisation techniques (essential)
- Experience in machine learning techniques as applied to asset allocation (desirable)
- Strong attention to detail and constant strive for excellence