AMNA NAEEM

Research-oriented data scientist with formal training in business analytics and applied experience working with heterogeneous, real-world datasets in sustainability, higher education, and public policy contexts. My work focuses on analysing complex socio-economic systems using data-driven methods, with particular attention to data quality, sectoral heterogeneity, and interpretability. I seek doctoral training in Data Science and Artificial Intelligence to develop deeper methodological expertise and to thoroughly evaluate and apply advanced analytical approaches within socio-economic systems.

• Applied Artificial Intelligence for Socio-Economic Systems: Using machine learning and AI-driven analytical methods to study productivity, sustainability, and public-sector systems, with attention to interpretability and real-world decision contexts.
• Data Quality, Bias, and Measurement: Investigating how data collection practices, aggregation choices, and sectoral heterogeneity influence model behaviour, inference, and policy conclusions in applied data science.
• Exploratory and Comparative Data Analysis of Complex Systems: Developing data-driven approaches to analyse heterogeneous administrative and institutional datasets, focusing on temporal trends, structural change, and uneven outcomes across sectors.

• Career Mentor Scheme
• Analysed longitudinal programme data across multiple cohorts to assess participation patterns, engagement trends, and operational outcomes.
• Produced evidence-based insights that informed training design, student engagement strategies, and programme planning.

• Exeter Award.
• Exeter Leaders Award.
• DataCamp / EU Smart Skills: Python, R, Statistical Regression

• Master’s Thesis — UK’s Productivity Puzzle (MSc Business Analytics, University of Exeter)
• Analysed the UK’s post-2008 productivity slowdown using Python, examining labour productivity hours and rates of change in relation to economic growth.
• Conducted exploratory and comparative analysis across four service sectors (health, education, food and beverage services, and computer programming) to identify uneven sectoral productivity patterns.
• Identified persistent productivity gaps relative to peer economies despite rising hours worked, with implications for evaluating productivity-focused policy interventions.