Biostatistician & Data Science Specialist

RENECO's mission is to implement a global conservation strategy aiming to restore and preserve the Houbara bustard populations of North Africa (Chlamydotis undulata) and Asia (Chlamydotis macqueenii). We deliver large-scale conservation, ecological monitoring, and applied research programmes across the Middle East, North Africa, Central Asia, and beyond. The organization combines conservation breeding programmes, long-term field expertise, and multidisciplinary scientific research to support evidence-based conservation strategies. Through its activities, RENECO generates and manages large and diverse datasets spanning ecology, demography, reproductive physiology, behaviour, veterinary science, conservation genomics, climate, and environmental monitoring. This strong research and data culture supports the development of advanced statistical, data science, and artificial intelligence approaches applied to wildlife conservation and biodiversity management. As part of our continued growth, we are seeking a dedicated Biostatistics/Data Scientist to join our team at Reneco Wildlife Consultancy, based in Abu Dhabi. This role combines advanced analytics, machine learning, AI, and operational implementation to deliver real-world impact and support conservation, environmental monitoring, and applied research initiatives Requirements: Education: PhD in Data Science, Artificial Intelligence, Machine Learning, Computer Science, or a related field. Professional Experience & Skills: 2–5 years of experience in Data Science, Machine Learning, and/or Artificial Intelligence (academic or industry). Proven experience developing and implementing AI/ML models on real-world datasets. Exposure to environmental, biological, ecological, or conservation-related datasets would be highly beneficial. Strong proficiency in Python and/or R for statistical analysis, machine learning, and data visualization. Strong proficiency in SQL and database management. Solid understanding of machine learning methodologies, model evaluation, and validation techniques. Ability to analyse, clean, and manage large and complex datasets. Expertise in data preprocessing, feature engineering, and analytical pipeline development. Familiarity with modern AI applications, including LLMs, AI agents, computer vision, and geospatial analytics. Knowledge of cloud computing environments, MLOps practices, Docker, and model deployment frameworks is an advantage. Strong analytical, problem-solving, and communication skills. Fluent English is required. French and/or Arabic would be a strong advantage. Show more Show less