I have a strong foundation in qualitative research, with experience designing, recruiting for, conducting, and analysing interviews. I’ve applied methods such as content analysis, narrative analysis, and thematic analysis to explore complex social issues. My PhD placed a strong emphasis on lived experience, using empirical evidence from real-life perspectives to better understand human behaviour. I am passionate about capturing rich, nuanced insights that help inform research, policy, and decision-making.

I have experience in quantitative analysis, using statistical methods and machine learning to extract insights from complex data. I’ve worked with large datasets, applying techniques like regression analysis, clustering, and classification to identify patterns and trends. With strong Python skills, I’ve built and pre-processed datasets, developed predictive models, and created data visualisations to communicate findings effectively. My work combines technical analysis with real-world applications, ensuring data-driven insights are meaningful and actionable.

I have hands-on experience applying computational linguistic methods to analyse and interpret language data. I’ve conducted analyses like creating an emotion detection classifier, using algorithms for demographic prediction, and performing psychological domain and corpus linguistic analysis. My work also includes building word embedding models, scraping and analysing Reddit data, and conducting topic modelling. Throughout my PhD, I applied these techniques to the exploration of risk-taking behaviours, transforming language data into evidence that can inform clinical practice and decision-making.

I have a knack for transforming complex information into clear, actionable insights. From publishing a systematic scoping review to conducting a rapid review during my internship with the Open Innovation Team, I’ve refined my ability to synthesise large volumes of data efficiently. I excel at identifying key themes, distilling findings, and presenting them in a meaningful way, ensuring research and evidence contribute effectively to decision-making and the bigger picture.

I love diving into data and uncovering hidden stories. With strong Python skills for analysis and visualisation, I’ve built and pre-processed large datasets from scraped online data to identify meaningful patterns. Whether it’s wrangling messy data, running statistical analyses, or creating compelling visuals, I enjoy transforming raw information into valuable insights. My work focuses on making data accessible, interpretable, and useful for research and decision-making.

I have experience mapping the policy landscape and turning research into actionable insights. During my internship with the Open Innovation Team, I reviewed existing evidence, developed early policy recommendations, and presented findings to senior officials. By exploring the policy implications of my PhD research, I’ve gained a strong understanding of how data-driven insights shape real-world decision-making, helping to bridge the gap between research, policy, and practical applications.