ABOUT THE ROLE

The institution generates a great deal of information and data across campuses, programmes and systems held by functions with different definitions and different reporting rhythms.

The Manager, Business Performance & Analysis will assemble it, identify what matters in it and put that in front of the people who have to decide. You are expected to find the insight inside fragmented institutional information and make it usable by the people who have to act on it.

This role goes beyond producing analysis to deciding what the analysis means, what can reasonably be concluded from it, and the form in which that meaning will actually land. The role will enable the University to act on what its own information is telling it by bringing together fragmented evidence, establishing what matters and putting it in front of the right person in a form they can use.

ROLE RESPONSIBILITIES

Research, Economic Analysis and Insight (the core of the role)

• Produce quantitative and economic analysis underpinning material submitted to the Executive Committee and the Board including scenario work, unit economics, capacity and cost modelling, cost behaviour, and the baseline scenario against which a decision should be judged.
• Take fragmentary, inconsistent and incomplete inputs from several functions and produce one defensible view, with the areas of remaining uncertainty named rather than smoothed over. This is the central skill of the role.
• Interrogate the question behind the request. Where the question as asked will not produce a useful answer, clarify the decision that needs to be supported and propose the more useful analytical question.
• Maintain relevant external evidence, including comparator institutions, sector benchmarks, published research and precedent relevant to current analyses and decisions.
• Build and maintain the external evidence base: comparator institutions, sector benchmarks, published research, and precedent relevant to decisions in front of ALU.
• State assumptions explicitly, and state plainly what an analysis does not establish. Where required data does not exist, publish the gap rather than substituting a proxy without saying so.
• Review recurring analysis and underlying assumptions, identify material gaps or inconsistencies, and raise findings where further scrutiny is required.

Communication and Decision-Ready Output

• Design data and insight presentations so the key finding is immediately visible rather than presenting the dataset in full. Treat chart selection, sequencing and emphasis as analytical choices.
• Determine the appropriate format for each piece of work, whether a written memo, one-page decision brief, chart, dashboard, model walkthrough or verbal briefing, based on the decision, audience and complexity involved.
• Write for senior readers with limited time: lead with the finding or decision required, support it with the relevant evidence, and state what remains unknown and what would resolve it.
• Adjust the level of detail, language and presentation across audiences including the Executive Committee, Board, departmental leaders and technical counterparts.
• Present analysis clearly and confidently, including the assumptions, limitations and implications of the work.

Automation and tooling

• Support the development and maintenance of automated workflows for data extraction, reconciliation, monitoring and reporting, reducing recurring manual effort.
• Use AI-enabled tools, agents and scripted workflows to accelerate recurring analytical tasks, including drafting, summarisation, exception detection, data preparation and scheduled checks.
• Apply judgement to AI-generated outputs, validate results and identify where automation should not replace human review.
• Build and maintain escalation triggers within recurring analytical and monitoring processes so that exceptions, overdue actions or material changes generate timely alerts.
• Document analytical tools and workflows to a standard that allows another team member to operate, understand and modify them without reliance on the original author.

Analytics Capability and Continuous Improvement

• Improve analytical methodologies, templates, reporting processes and tools used by the Business Performance & Analysis function.
• Identify recurring requests and manual processes that can be standardised, automated or redesigned.
• Establish practical quality checks for recurring analysis and reporting.
• Share analytical methods, tools and lessons learned across the team and support the development of analytical capability.

Performance Tracking and Follow-through

• Maintain accurate records of key decisions, actions, owners, assumptions and review dates arising from performance and planning activities.
• Monitor agreed actions and escalation triggers and raise overdue or off-track items appropriately through appropriate and well suited digital tools.
• Maintain the document repository to support continuity, transparency and effective handover.

Executive and cycle coordination

• Coordinate engagement requirements against the University's planning, review and reporting calendar, including committee dates, planning cycles, statutory requirements and reporting deadlines.
• Prepare the analysis, briefing material and supporting information required for those engagements.
• Track actions and commitments arising from key meetings and ensure they are reflected in the appropriate working records and follow-up mechanisms.
• Coordinate inputs across functions to ensure materials are complete, consistent and ready for review.

Ensure the safety and well-being of all young persons and vulnerable adults with whom we deal, by adhering to ALU’s comprehensive safeguarding policy, maintaining a zero-tolerance approach to any violations of safeguarding standards

REQUIRED EXPERIENCE

• 6 to 8 years of relevant professional experience, with demonstrated experience producing analysis that informs decisions taken by people more senior than yourself, in a research institution, consultancy, financial institution, policy body, university, or comparable analytical function.
• Economic reasoning, evidenced by training or by practice. You think in terms of opportunity cost, marginal effects, incentives and counterfactuals, and you state your assumptions before your conclusions. A degree in economics or experience across consulting, policy research, financial analysis or a quantitative discipline.
• Demonstrated ability to turn complex analysis into concise executive communication, including decision briefs, presentations, dashboards or written recommendations.
• Working competence in Python or R for data analysis and evidence of having built or supported an analytical workflow that can run with limited manual intervention.
• Practical experience using AI-enabled tools, including large language models, agents or scripted workflows, to improve analytical productivity, automation or decision support.
• Demonstrated ability to take incomplete and conflicting inputs from multiple sources and produce a single defensible view.
• Demonstrated ability to become productive quickly in an unfamiliar subject area and build sufficient domain understanding to analyse it effectively.
• Experience working across functions with different data sources, priorities and perspectives is strongly preferred.
• Exposure to executive, Board or committee reporting is desirable.

REQUIRED COMPETENCIES

Functional Competencies

• Analytical Reasoning: Break down complex questions, identify the relevant variables and determine the analysis required to support a decision.
• Data Interpretation and Judgement: Analyse information critically, identify patterns and anomalies, test assumptions and assess what can reasonably be concluded.
• Economic and Financial Acumen: Understand opportunity cost, incentives, cost behaviour, revenue, contribution and the financial implications of decisions.
• Modelling and Forecasting: Build and interpret scenarios, forecasts and sensitivity analyses to support planning and decision-making.
• Digital and AI Fluency: Use analytical, automation and AI-enabled tools confidently to improve speed and quality while independently validating outputs.
• Executive Communication: Distil complex analysis into clear findings, implications and recommendations appropriate to the audience and decision required.

Behavioural & Leadership Competencies

• Judgement: Recognise what matters, make sound calls with imperfect information and know when further analysis is warranted.
• Constructive Challenge: Question assumptions, identify weaknesses and raise material concerns directly and objectively.
• Ownership: Take responsibility for analytical quality, timeliness and usefulness from problem definition through to delivery.
• Collaboration: Work effectively across Finance, Data/IT, academic and professional functions while respecting ownership of underlying data and business processes.
• Delivery Discipline: Manage competing demands, coordinate contributors and deliver high-quality work to agreed timelines.
• Continuous Improvement: Improve processes, tools and ways of working so that recurring work becomes more efficient, reliable and scalable.

Originally posted on Himalayas