أبلاي إيدج ابدأ البحث عن عمل

Child Psychology / Behavioural Science Lead

Birbal AI · Abu Dhabi Emirate, United Arab Emirates

قدّم وتابع مع أبلاي إيدج
Key ResponsibilitiesSelect and adapt evidence-based, internationally validated behavioural observation frameworks appropriate for children and young people across age bands, contextualised for UAE cultural and social settings.Define the indicator taxonomy (engagement, distress-consistent, calm-consistent, interaction patterns) with per-age-band validity boundaries, documented evidence base, and known limitations.Work daily with AI/ML engineers to translate behavioural science into model requirements, labels, and validation criteria — and to keep claims scientifically defensible.Design and run the expert-validation protocol: structured review sessions where professionals rate AI observations against their own judgement (the ground truth).Shape the user experience: indicator wording, confidence presentation, and on-screen guidance that reinforces professional interpretation.Advise on ethics, safeguarding, and consent design; contribute to impact assessments and governance reviews.QualificationsAdvanced degree (PhD or Masters + licensure) in child/developmental psychology, clinical psychology, behavioural science, or closely related field.7+ years in child behavioural assessment, developmental assessment, or applied behavioural research — including direct assessment experience with children.Working knowledge of structured observation instruments and their psychometric properties (validity, reliability, cultural adaptation).Demonstrated ability to collaborate with technical teams (digital health, ed-tech, assessment technology, or research software).Fluent professional English with excellent scientific writing; comfort presenting to expert review panels.Preferred Skills:Experience in the GCC or with Arabic-speaking populations; Arabic language skills. Publications or applied work on observational/behavioural coding (e.g., FACS-informed methods, engagement coding, attachment-informed observation). Prior involvement in AI/ML, digital assessment, or affective-computing projects, including their ethical critique.