Application Open:
Full-Time
Job Purpose:
The Data Scientist will take the responsibility to analyze and interpret complex datasets in the university into valuable academic information, build and operate analytical and AI services with multiple data sources, develop and implement KPIs based on business owner definitions to support actionable insights and strategic decision-making across the university, to achieve institutional excellence. This role offers the opportunity to advance MBZUAI development, drive innovation, and ensure scalability as we grow.
Key Responsibilities:
Data Analysis & Interpretation:
- Conduct comprehensive analysis of university data from multiple sources, including student information systems, research databases, financial systems, and operational metrics.
- Apply statistical methods and machine learning techniques to extract meaningful patterns and insights from large, complex datasets.
- Develop predictive models to forecast trends in enrollment, research outcomes, resource utilization, and institutional performance.
- Create data-driven recommendations to support strategic planning and operational improvements.
Dashboard & Visualization Development:
- Create interactive, real-time dashboards that provide comprehensive views of university performance metrics.
- Design user-friendly visualizations that make complex data accessible to diverse stakeholders.
- Develop automated reporting systems that deliver timely insights to key decision-makers.
- Ensure dashboard accuracy, performance, and user experience with continuous optimization.
KPI Development & Management:
- Collaborate with business owners across departments to define and refine key performance indicators.
- Establish data collection methodologies and measurement frameworks for institutional KPIs.
- Create parameter-based KPI systems that support flexible metric definition and tracking.
- Develop benchmarking systems to compare university performance against peer institutions and industry standards.
Data Infrastructure & Quality:
- Work with IT teams to ensure robust data pipelines and integration across university systems.
- Implement data quality assurance processes and validation procedures.
- Maintain data governance standards and ensure compliance with privacy regulations.
AI Services Development & Operations Support:
- Work with AI Engineer teams to provide data analytics support in AI chatbot, machine learning models, and AI solutions data requirements that enhance university operations.
Data Compliance & Ethics:
- Adhere to data governance policies, privacy regulations (e.g., GDPR, FERPA), and ethical guidelines during retrieval.
- Secure sensitive data through encrypted transfers and access controls.
Other Duties:
- Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Academic Qualification:
- Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field.
Professional Experience:
Essential
- Minimum 3-5 years of experience in data science, analytics, or related fields.
- Coding and Debugging: Strong coding capability and coding quality sense in addition to excellent debugging skills.
- Statistical Analysis: Proficient experience in using AI technologies, statistical methods, hypothesis testing, and experimental design to perform data analysis for business purposes.
- Data Visualization: Hands-on Experience with Tableau, Power BI, or similar tools to visualize data proper visualization.
- Cloud Platforms: Good Knowledge of Snowflake, AWS, Azure, or Google Cloud Platform for data processing and maintenance.
- Database Management: Expertise in relational and NoSQL databases such as MySQL, PostgreSQL, MongoDB, or Redis for data storage.
- Analytical & Communication Skills: Excellent problem-solving abilities with attention to detail to handle and resolve customer requirements. Strong communication skills and ability to explain complex technical concepts to non-technical stakeholders. Collaborative mindset and capability to work effectively with cross-functional teams.
- Language: Strong English proficiency at both technical and business parts, with fluency in additional languages as a plus.
Preferred
- Have experience in higher education or research institutions.
- Knowledge of educational data mining and analytics.
- Familiar with research data management and academic workflow/system.
- Understand higher education metrics and accreditation requirements.
