COMPANY DESCRIPTION
Singapore Management University is a place where high-level professionalism blends together with a healthy informality. The ‘family-like’ atmosphere among the SMU community fosters a culture where employees work, plan, organise and play together – building a strong collegiality and morale within the university.
Our commitment to attract and retain talent is ongoing. We offer attractive benefits and welfare, competitive compensation packages, and generous professional development opportunities – all to meet the work-life needs of our staff. No wonder, then, that SMU continues to be given numerous awards and recognition for its human resource excellence.
RESPONSIBILITIES
- This position is for SMU Executive Development. (SMU ExD)
- Training Management System (TMS) Migration & Systems
- Support theTMS migration and implementation.
- Liaise with central IT, vendors, and internal stakeholders to ensure successful rollout.
- Translate business requirements into system specifications.
- Support user acceptance testing (UAT), data validation, and system configuration.
- Drive user adoption, training, and post-implementation support.
- Identify and resolve system issues and continuously enhance system performance.
- Learning Data, Surveys & Insights
- Support programme impact surveys across key programmes.
- Analyse survey data to generate actionable insights for programme improvement.
- Contributes to end-of-programme analysis, including detractor and sentiment insights.
- Present findings to internal stakeholders to support evidence-based decision‑making.
- Apply AI-enabled analysis (e.g., summarisation, theme detection) to synthesise survey. feedback and qualitative comments into actionable insights.
- Identify and implement automation/AI opportunities to reduce manual reporting and improve turnaround time (with appropriate data governance).
- Dashboard Development & Data Projects
- Design, develop, and maintain dashboards to track. Programme performance and outcomes.
- Faculty and programme director workload.
- Sales and pipeline performance (in collaboration with relevant teams).
- Lead development of data assets such as programme databases and site visit repositories.
- Support in department’s measurement of success metrics (e.g. balanced scorecard).
- Ensure data integration and consistency across systems (e.g., TMS, CRM, survey platforms).
- Systems & Process Improvement
- Identify opportunities to enhance operational efficiency through systems and process improvements.
- Support enhancements, bug resolution, and feature expansion for internal systems (e.g., FMS or equivalent).
- Work with stakeholders to redesign workflows enabled by new systems and tools.
- Database Management & Data Governance
- Maintain and update faculty and programme databases.
- Ensure data accuracy, integrity, and compliance with institutional policies.
- Establish and promote best practices in data management and usage.
- Ad‑hoc Reporting & Analysis
- Produce regular and ad‑hoc reports for leadership and stakeholders.
- Support business reviews, programme evaluations, and strategic initiatives.
- Respond to data and reporting requests in a timely and structured manner.
- Any other task assisgned.
QUALIFICATIONS
- Bachelor’s degree in Business, Data Analytics, Information Systems, or related field.
- Minimum 5 years of relevant experience in learning operations, data analytics, or systems implementation.
- Experience working with learning management systems (LMS/TMS), CRM platforms, or enterprise systems.
- Experience using AI‑enabled tools for analysis, reporting, or process improvement is an advantage.
- Familiarity with dashboarding and data visualization tools (e.g., Power BI, Tableau).
- Experience with survey tools (e.g., Qualtrics) is an advantage.
- Strong analytical and problem‑solving skills, with the ability to translate data into insights.
- Ability to bridge technical and non‑technical stakeholders effectively.
- Project management capabilities, particularly in system implementation or transformation initiatives.
- High attention to detail and commitment to data quality.
- Strong communication and presentation skills.
- Proactive, adaptable, and comfortable working in a dynamic environment.
- AI literacy: ability to use AI‑enabled productivity and analytics tools to improve workflows, with sound judgment on data privacy, accuracy, and limitations.
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