Team: Quantitative Research & Analysis, Multi-Asset Solutions

Division: Multi-Asset Solutions, CBO

State Street Global Advisors is a global leader in providing investment management services to clients worldwide. With ~$4.1 trillion of assets under management, SSGA has the size, scale and global perspectives to provide insightful solutions to meet the requirements of our clients. Please visit www.statestreet.com for further corporate reports and press releases.

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.

• Conduct research and analysis independently
  
  
  • Independently conduct portfolio research and analysis to showcase the application of ETFs and other investment funds in asset allocation contexts, leveraging advanced quantitative tools (most notably ,Python, R).
  • Develop asset allocation strategies using robust statistical and econometric techniques

• Service client requests through providing bespoke performance analytics
  
  
  • Provide tailored performance analytics in response to client requests
  • Maintain a strong client focus, particularly for EMEA and APAC regions, catering to a diverse range of client types, including intermediaries, asset managers, insurers.
  • Collaborate with other colleagues to prepare custom research presentations on topics such as portfolio construction and quantitative investment strategies.
  • Deliver analytics that demonstrate the effectiveness of in-house strategies.

• Create market-leading research publications
  
  
  • Author market-leading research papers using SPDR ETFs as foundational building blocks.
  • Publish work in peer-reviewed industry journals and research papers specific to European and Asian audiences.
  • Conduct asset allocation research using statistical and machine learning methods, with a particular focus on time series techniques.

• A degree (Bachelor's, Master's, or PhD) in a highly quantitative field (STEM), such as Physics, Engineering, Computer Science, Mathematics, Financial Engineering, Statistics or Data Science, among others. (essential)
• Expert proficiency in Python (essential) with a proven track record of solving quantitative problems; familiarity with R is desirable. (These skills will be assessed during the interview.)
• At least 3-5 years of experience in finance, preferably on the buyside. Exceptional candidates with strong skills may be considered even without meeting this requirement.
• Familiarity with portfolio optimization and statistical, machine learning, and deep learning techniques, including reinforcement learning, as applied to asset allocation (highly desirable).
• A demonstrated interest in investment management, particularly in asset allocation, optimisation, and the implementation of investment views.
• Exceptional attention to detail and a strong commitment to excellence.
• Fluency in additional European languages (e.g., French, German, Spanish, or Italian) (desirable)
• Additional certifications such as CFA, CQF, CAIA, or FRM are advantageous. Completion of, or a commitment to complete, one of these professional qualifications is highly valued
• Familiarity with database languages such as SQL (desirable)
• Comfortable engaging with clients at all levels and building strong professional relationships