Research Associate -AI-Data Science & Forecast Specialist

University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied bachelor’s and master’s degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more.

With more than 700 staff and over 8,000 students, UDST is the destination for top-notch applied and experiential learning. The University is recognized for its student-centred learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences with the incorporation of innovative technological interventions, to further enhance students’ skills and help develop talented graduates that can effectively contribute to a knowledge-based economy and make Qatar’s National Vision 2030 a reality.

We are seeking a highly motivated Research Associate to join a collaborative research project between the University of Doha for Science and Technology (UDST) and a leading technology-driven business in Qatar. The ideal candidate will have a strong background in data engineering, machine learning, forecasting, and MLOps, with experience in building scalable data infrastructure and deploying predictive models into production environments.

Key Responsibilities:

1. Data Infrastructure & Collection

• Design and implement ETL pipelines for data ingestion and transformation.
• Perform data audits, implement quality checks, and maintain comprehensive data documentation.

2. Forecasting Module Development

• Develop and benchmark advanced time-series forecasting models.
• Train and fine-tune models with scalable frameworks, ensuring performance against business KPIs.
• Prototype, validate, and document predictive models.

3. Microservices, MLOps & Deployment

• Integrate forecasting modules with feature stores and existing data pipelines.
• Containerize models using Docker and set up CI/CD pipelines for deployment.
• Implement monitoring dashboards, drift detection, and alerts to ensure reliable system performance.

4. Documentation & Knowledge Transfer

• Prepare full technical documentation, including API specifications and user guides.
• Develop training materials, workshops, and modules for knowledge transfer.

• Educational Background:

• Ph.D. in Computer Science, Data Engineering, Machine Learning, or a related field, OR
• Master’s degree in Computer Science/Engineering with at least 3 years of industry experience in Data Infrastructure, Forecasting, and MLOps.

• Technical Expertise:

• Strong knowledge of time-series forecasting, machine learning, and predictive analytics.
• Proficiency in ETL pipelines, data quality assurance, and integration of external datasets.
• Hands-on experience with MLOps tools (Docker, CI/CD, monitoring, drift detection).
• Familiarity with cloud-based environments (AWS, GCP, or Azure) is highly desirable.

• Programming Skills: Strong programming skills in languages such as Python or R. Familiarity with machine learning libraries and frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) is a must.
• Analytical Skills: Demonstrated ability to analyze complex datasets and develop data-driven solutions.
• Communication Skills: Excellent written and verbal communication skills, with the ability to convey complex technical concepts to non-technical audiences.
• Team Collaboration: Proven experience working in collaborative team environments and managing multiple tasks effectively.