Senior AI & Data Scientist (or Machine Learning Engineer)

Company Description The University of Tehran is a leading public research institution and one of the most historic universities in Iran, with roots extending back seven centuries. Officially inaugurated in 1934, it has grown into a comprehensive university offering 976 programs across 500 fields in 39 faculties and 120 departments on multiple campuses. The university is supported by more than 2,000 tenured faculty members and hosts over 40 research centers and 3,500 laboratories, providing a rich environment for advanced research and innovation. Recognized by the Ministry of Science, Research, and Technology, approximately 15% of Iran’s Centers of Excellence are affiliated with the University of Tehran. The institution also maintains a leading academic press, publishing over 50 peer-reviewed scientific journals and fostering a vibrant scholarly community.

Role Description This full-time Senior AI & Data Scientist (or Machine Learning Engineer) role is a hybrid position based in England, United Kingdom, with flexibility for partial work from home. The role involves designing, implementing, and evaluating machine learning and AI models to support research projects and institutional initiatives, including data-driven decision-making and advanced analytics. Responsibilities include collecting and preprocessing complex datasets, performing exploratory data analysis, building predictive and prescriptive models, and creating clear data visualizations for technical and non-technical stakeholders. The successful candidate will collaborate with interdisciplinary teams, contribute to scientific publications and reports, and help define best practices for data governance, reproducible research, and model deployment. The role may also include mentoring junior researchers or students, participating in grant proposals, and staying current with emerging AI and data science methodologies.

Qualifications

• Strong expertise in core data science disciplines, including Data Science and Data Analytics.
• Solid foundation in quantitative methods, particularly Statistics and Data Analysis.
• Ability to communicate insights effectively through Data Visualization and related tools.
• Proficiency with programming languages commonly used in AI and data science (such as Python or R) and experience with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn).
• Experience designing, training, and validating machine learning and deep learning models, including supervised and unsupervised approaches.
• Background in working with large, complex, or heterogeneous datasets, including data preprocessing, feature engineering, and model evaluation.
• Advanced degree (Master’s or PhD) in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related discipline, or equivalent professional experience.
• Ability to work effectively in interdisciplinary teams, communicate clearly in writing and presentations, and manage multiple projects in a hybrid work environment.
• Experience with cloud or high-performance computing environments, MLOps practices,