SPIVA Analytics Lead - Index Investment Strategy in London

SPIVA Analytics Lead - Index Investment Strategy in London

London Full-Time 60000 - 75000 Β£ / year (est.) No working from home possible
S&P Global

At a Glance

  • Tasks: Lead data-driven analytics for SPIVA Scorecards and collaborate globally on major benchmarks.
  • Company: Join S&P Dow Jones Indices, a leader in financial market analytics.
  • Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
  • Other info: Dynamic team environment with opportunities to work across international offices.
  • Why this job: Make an impact in global finance with your analytical skills and expertise.
  • Qualifications: Experience in data analysis and a strong understanding of investment strategies.

The predicted salary is between 60000 - 75000 Β£ per year.

S&P Dow Jones Indices is seeking an experienced data-driven professional to join the Index Investment Strategy team in London.

The role focuses on SPIVA Scorecards and related analytics, owning data sources, validation, and delivery.

You will work with colleagues across London, Hong Kong, and New York on major benchmarks.

You will operate with autonomy, advise on data, methodology, and research questions, and contribute to scalable analytical systems used globally.

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SPIVA Analytics Lead - Index Investment Strategy in London employer: S&P Global

S&P Global is an excellent employer, offering a vibrant work culture that prioritises innovation and client success in the heart of Greater London. Employees benefit from generous perks, professional development opportunities, and a collaborative environment that fosters growth and leadership. Joining S&P Global means being part of a forward-thinking team dedicated to delivering exceptional solutions in the financial sector.

S&P Global

Contact Details:

S&P Global Recruitment Team

We think you need these skills to ace SPIVA Analytics Lead - Index Investment Strategy in London

Communication Skills
Problem-Solving Skills
Python
SQL
Automation
Attention to Detail
Data Engineering