Manager, Data Analytics

The Student and Academic Services Department (SASD) is a dedicated team committed to delivering comprehensive support across the entire student life cycle—from admission and matriculation to graduation and beyond the classroom. SASD works collaboratively with schools, colleges, and autonomous institutes to ensure a seamless and enriching academic journey for all students. This position sits with the Digital Innovations Team (DIT) which is responsible for developing AI solutions, automating administrative processes, and delivering data‑driven insights that improve both staff efficiency and student experience.

The successful candidate will have strong hands‑on capabilities to strengthen the team's ability to deliver machine learning, NLP, forecasting, recommendation, and GenAI‑enabled use cases. You will work closely with the team lead, business stakeholders, and colleagues responsible for technical implementation to translate operational challenges into practical AI and data solutions. You will be expected to bring hands‑on technical depth in Python, data analysis, and machine learning, while also being able to explain your approach and findings clearly to non‑technical audiences.

The successful candidate would be an individual who thrives in navigating ambiguity, asking insightful questions, validating assumptions with data, and building prototypes that evolve into practical, scalable solutions.

Key Responsibilities

• Stakeholder Engagement & Solution Support
• Work with the team lead and business units across the university to understand operational challenges and translate them into well‑defined problem statements, data requirements, and AI/ML solution approaches.
• Contribute to the design of AI and digital solutions, with primary ownership of the data science, machine learning, NLP, and model evaluation components.
• Communicate analytical findings, model results, and technical recommendations clearly to both technical and non‑technical stakeholders, and support management‑level updates where required.
• Help prepare and consolidate requirements documentation and solution design artefacts as needed.
• AI & Data Science Project Delivery
• Lead the AI/ML and data science projects, including problem framing, data exploration, feature analysis, model development, testing, evaluation, and implementation.
• Apply appropriate techniques based on project needs, including predictive modelling, forecasting, NLP, and recommendation systems.
• Collaborate closely with the team's automation and engineering specialist to integrate AI/ML components into automation workflows and operational solutions.
• Conduct exploratory data analysis and translate findings into actionable insights and practical recommendations.
• Support the development and evaluation of GenAI‑enabled solutions, including use cases involving summarisation, classification, semantic search, document understanding, and decision support; contribute to RAG pipelines through data preparation, retrieval evaluation, prompt testing, and response validation.
• Work with AI agent frameworks and agentic workflows, including multi‑step reasoning, tool use, and orchestration patterns; experience building AI agents is a strong advantage.
• Design test cases and evaluation approaches to assess AI output quality, including relevance, correctness, grounding, consistency, and hallucination risk.
• Vendor & External Partnership Support
• Provide technical input on vendor proposals, especially on AI/ML methodology, data requirements, model evaluation, and feasibility.
• Support vendor discussions by helping to clarify AI / data science requirements, validation criteria, and acceptance test scenarios.
• Team Contribution & Knowledge Sharing
• Provide technical guidance and knowledge sharing to junior team members in data analysis, Python, AI/ML testing, and model evaluation.
• Stay current with developments in AI, machine learning, and digital innovation, and proactively identify opportunities to apply new approaches within the team's project portfolio.

Requirements

• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, or a related field; postgraduate qualifications in AI/ML or software engineering are an advantage.
• 5–8 years of professional experience, with at least 3 years in data science, machine learning, applied analytics, or AI‑related work.
• Demonstrated experience delivering AI/ML or data science projects, including data preparation, modelling, evaluation, and communication of results.
• Strong Python and SQL proficiency, with hands‑on experience using data science libraries such as pandas, NumPy, and scikit‑learn.
• Practical experience applying ML or statistical techniques to real‑world