Data Scientist

The University of Southampton is seeking a Data Scientist to join Strategic Planning & Analytics and support work focused on strengthening Southampton’s reputation and performance in external rankings.

This new role will help the University build a richer, data-driven understanding of how reputation is formed, measured and influenced. Working within a dedicated team, you will develop evidence, models and tools that support more effective targeting, prioritisation, decision-making and impact measurement across the University.

Your primary focus will be to develop predictive modelling of reputation drivers and outcomes, while also improving the University’s analytical tools, methods and use of data. You will bring together complex data from a range of internal and external sources, applying appropriate analytical and statistical methods to identify patterns, test assumptions, model potential outcomes and generate actionable insight.

You will work with datasets relating to university rankings, reputation indicators, peer and competitor performance, survey outcomes, research and education metrics, and other relevant external and internal sources. This may include data cleaning and transformation, exploratory data analysis, segmentation or clustering, predictive modelling, trend forecasting, scenario modelling, sensitivity analysis, benchmarking and interpretation of composite or weighted indicators.

This is an opportunity to apply data science to work with direct strategic relevance. You will be part of Strategic Planning & Analytics, the University’s central data reporting, insight and analysis team, working in a collaborative analytical environment that brings together expertise in data science, advanced analytics, forecasting, market insight and business intelligence.

We are looking for someone who combines technical analytical capability with excellent judgement, curiosity and communication skills. You will be confident working with complex, incomplete or inconsistent datasets, assessing data quality and limitations, selecting appropriate methods, and translating quantitative analysis into clear insights, options and recommendations for decision-makers.

You will need to be comfortable working across organisational boundaries, collaborating with colleagues in professional services and faculties, and communicating technical findings clearly to non-specialist audiences. Experience of university rankings, league tables, reputation analysis, higher education data or bibliometrics would be valuable, but we are also interested in candidates who can bring strong data science and analytical experience from other relevant contexts.

About you

You will bring:

• strong numeracy and experience of analysing complex data from multiple sources;
• knowledge of statistical modelling, predictive analytics, data science or machine learning methods;
• experience integrating, cleaning, validating and analysing complex datasets;
• the ability to assess data quality, uncertainty, limitations and appropriate use;
• experience using tools such as Excel, Python, R, SQL, Alteryx or equivalent;
• experience translating quantitative analysis into clear insight, options and recommendations;
• the ability to communicate technical findings clearly to non-specialist audiences;
• curiosity, creativity and rigour in using data to solve complex problems;
• awareness of data protection, data governance and ethical use of data.

Experience of rankings, league tables, reputation surveys, higher education performance metrics, bibliometrics, external datasets such as QS, THE, REF, HESA, NSS, Graduate Outcomes, or data visualisation tools such as Power BI or Tableau would be an advantage.

You will join a supportive and ambitious team within the world-leading, research-intensive University of Southampton, a founding member of the Russell Group and ranked in the top 1% of universities worldwide.

You will have the opportunity to apply data science to a high-profile strategic priority, develop expertise in reputation & rankings analytics, and contribute directly to evidence-based decision-making across the University.

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