Lead Data Analyst (Rankings)

The role of Lead Data Analyst (Rankings) involves implementing real change in the international higher education landscape by taking responsibility for the technical implementation, testing and production infrastructure of QS’s rankings capability. This includes developing new analytical methods, leading technical aspects of rankings production, and managing and mentoring data analysts and engineers.

Requirements

• Postgraduate degree in Mathematics, Statistics, Econometrics, Data Science, or a closely related quantitative discipline from a leading university
• Clear evidence of organisational impact, leadership and driving transformation across legacy business and data operations
• Proven experience in data analytics, or a proven accelerated career trajectory demonstrating readiness for a lead-level role
• Demonstrable expertise in applied statistics: multivariate analysis, composite indicator construction, normalisation methodologies, regression modelling, and uncertainty quantification
• Strong proficiency in SQL
• Proficiency in Python for statistical analysis, simulation, and model development
• Experience working with large, complex, multi-source datasets in a production analytical environment
• Ability to present technical and statistical content confidently to non-specialist audiences
• Exceptional story-telling and presentation design skills
• Strong written communication skills, including the ability to produce rigorous methodology documentation and stakeholder-facing technical papers

Benefits

• Competitive base salary
• Access to an annual bonus scheme (for qualifying roles only)
• 25 days annual leave, plus bank holidays – increasing to 27 days after 5 years’
• Access to a Buy Holiday scheme allowing you to buy up to 5 additional holiday days per year
• Enhanced maternity and paternity leave
• Generous pension through Royal London
• Comprehensive private medical insurance and wellness scheme through Vitality
• Cycle to work scheme
• A vibrant social environment and multicultural and multinational culture

Tagged as: Data Analysis