Dylan Dineen
I am a social scientist with an MA in Socio-Cultural Anthropology and a BA in Anthropology and Philosophy.
About
I am a social scientist with an MA in Socio-Cultural Anthropology and a BA in Anthropology and Philosophy. I am skilled in qualitative research (including interviewing and coding), cultural and critical analysis, fieldwork and problem-solving, with experience applying these across diverse contexts.\n\nMy philosophy background strengthens my research with critical reasoning, logical clarity and ability to interpret complex social issues through multiple perspectives. I am adaptable, collaborative and motivated to apply academic and analytical skills to professional environments with strong research ethics and informed-consent practice. I am a clear communicator, both oral and written, with strong capability to synthesise complex ideas and present them clearly to different audiences. Key research interests include religion and secularism in contemporary Ireland; youth culture, morality and identity formation; technology, privacy and digital ethics; policy, governance and cultural change; and qualitative and phenomenological research methods.
- 2 years' experience
- Joined January 2026
Industry experience
- Education
- Professional Services
- Media & Entertainment
- Other
- Software & Internet
Work experience· 11 roles
Supervised and cared for international children and teenagers in residential settings and on trips across Ireland, ensuring their well-being and safety. Organized, implemented, and led a wide range of activities, including music workshops, sports, and cultural excursions, encouraging active participation and engagement. Fostered a positive, inclusive environment that supported English language learning and Irish cultural immersion.
Training AI models through structured data labeling, annotation, and quality assurance; identifying outliers to enhance model robustness and reduce biases; providing feedback on AI-generated outputs to refine accuracy; supporting data evaluation and content review for large-language-model training; applying critical reasoning to assess cultural nuance, ethics, and bias in datasets; strengthening understanding of how social and linguistic context informs AI systems.
Training AI models through structured data labelling, annotation, and quality assurance. Identifying outliers to enhance model robustness and reduce biases. Providing feedback on AI-generated outputs to refine accuracy. Supporting data evaluation and content review for large-language-model training. Applying critical reasoning to assess cultural nuance, ethics, and bias in datasets. Strengthening understanding of how social and linguistic context informs AI systems.
Education
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