• Analyse large and complex datasets from Annual Impact Measurement (AIM) surveys at the global level and the field office level using advanced statistical modelling techniques (e.g., regression, GLM, causal inference, propensity score matching, etc.):

• Perform exploratory data analysis to understand the structure and content of AIM datasets.
• Clean and preprocess datasets to ensure accuracy, consistency, and readiness for analysis.
• Conduct hypothesis testing guided by sector leads and key stakeholders’ programmatic decisions.
• Apply advanced statistical models (e.g., regression, GLM, causal inference) to uncover meaningful patterns, relationships, and impact drivers in AIM data.
• Develop and implement predictive models (e.g., time series analysis) to forecast trends and support data-driven decision-making.
• Collaborate closely with data analysts from the AIM team to ensure insights are shared and address complex data challenges.

• Create and maintain dashboards and reports to communicate findings to stakeholders:

• Design and implement interactive, user-friendly dashboards that present key results.
• Draft clear, concise, and visually appealing summary reports for diverse audiences, including internal teams and external stakeholders.
• Update dashboards and reports regularly to reflect the latest data and analysis results.
• Develop presentation materials that effectively convey findings and insights to both technical and non-technical audiences.

• Interpret and translate statistical insights into actionable recommendations for non-technical audiences:

• Simplify complex findings into accessible narratives using clear language and visuals (e.g., charts, infographics, and diagrams).
• Facilitate workshops or training sessions to help stakeholders interpret data and apply findings to decision-making.
• Create guidance documents or user manuals for dashboards and reports to ensure continued usability by stakeholders.
• Work closely with Research departments to provide insights on prioritised business needs for analysis and reporting

• Minimum 6-8 years of experience in statistical modelling with a track record of delivering actionable insights.
• Master’s or Ph.D. in Statistics, Data Science, Economics, or a related quantitative field.
• Proficiency in statistical programming languages such as R or Python for data analysis and modelling
• Expertise in SPSS for advanced statistical analysis and reporting
• Strong understanding of causal inference techniques
• Familiarity with data visualization tools, Power BI, for creating intuitive and interactive dashboards.
• Experience with survey design and data collection tools, KoboToolbox, to support high-quality data gathering and management.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills to effectively present complex analyses and insights to both technical and non-technical audiences.
• Effective in written and verbal communication in English.
• Available for travel up to 15% of the time.
• Preferred Knowledge and Qualifications

• Familiarity with machine learning techniques and their application in impact measurement or predictive modelling.
• Knowledge of survey methodology and best practices in data quality assurance.
• Understanding of impact frameworks and their alignment with statistical analysis.