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The Head of Data Analysis at Leeds Policy Institute (LPI) supports the organisation's research initiatives across its policy areas by providing expert data analysis. You will lead a dedicated team responsible for conducting statistical analysis, regressions, and generating visualisations, and offering data-driven insights to enhance the quality and impact of LPI's research output.

Key Responsibilities:

• Data Analysis and Interpretation: Lead the analysis of complex datasets to extract meaningful insights and inform research findings across LPI's policy areas. Utilise statistical and econometric techniques relevant to LPI's research objectives.
• Software Proficiency: Demonstrate proficiency in data analysis software tools, such as R, Python, Stata, Excel, and Macrobond to conduct quantitative analysis and produce visualisations tailored to the needs of research teams. 
• Visualisation and Reporting: Collaborate with research area heads and team members to develop visually compelling graphs, charts, and dashboards that effectively communicate key findings. Ensure visualisations are clear, informative, and aligned with the requirements of research publications and presentations.
• Research Support: Provide expert guidance and support to research teams in navigating data-related challenges, clarifying mathematical concepts, and interpreting statistical results. Assist in designing research methodologies, survey instruments, and data collection strategies to maximise data quality and relevance.
• Communication and Collaboration: Maintain open communication channels with Policy area heads and the society committee to understand research priorities, requirements, and deadlines. Collaborate effectively with interdisciplinary teams to integrate data analysis seamlessly into research projects.
• Team Leadership and Development: Lead and mentor a team of data analysts and researchers, fostering a collaborative and supportive working environment. Provide training, guidance, and feedback to team members to enhance their technical skills, analytical capabilities, and professional growth.
• Quality Assurance: Ensure the accuracy, reliability, and validity of data analysis outputs by implementing quality assurance measures and validation protocols. Conduct peer reviews and audits to identify potential errors, inconsistencies, or biases in data analysis processes and results.
• Ethical Data Practices: Uphold ethical standards and data privacy principles in the collection, storage, and analysis of research data. Ensure compliance with relevant regulations, guidelines, and institutional policies governing data management and research ethics.