Senior Assistant Director/ Assistant Director, Data Analytics
AIC · Singapore
About this role
The Senior Assistant Director/ Assistant Director, Data Analytics will lead advanced quantitative analytics, modelling, and programme evaluation initiatives to inform policy and operational planning at AIC. The role focuses on ensuring rigorous, defensible data-driven insights to support business intelligence and operational decision-making. Responsibilities include: Programme Evaluation: Facilitating the development of theories of change, logic models, study designs, and analytic plans. Producing and supervising evaluation reports summarizing methodology and findings. Business Intelligence: Developing and overseeing data products including dataset extraction, dashboards, geospatial maps, and machine learning-based analyses. Providing descriptive statistics, projections, and insights to challenge assumptions and validate program initiatives. Consulting and advising internal divisions on outsourced evaluations. Requirements: A degree in Data Science, Business Analytics, Statistics, Mathematics, or a related field. At least 10 years of relevant experience in providing data support to business units, preferably in health or social care settings. A minimum of 4 years of supervisory management experience. Proficiency in Python, Stata, or R, and data visualization tools like Tableau or Power BI. Strong knowledge of statistics and quantitative methods. Ability to manage multiple projects under tight deadlines and handle ambiguity while maintaining a focus on business objectives.
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Skills for this role
Data ScienceBusiness AnalyticsStatisticsMathematicsPythonStataRTableauPower BIMachine LearningData VisualizationQuantitative AnalyticsProgramme EvaluationGeospatial Mapping
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A focused preparation guide, built around this job’s responsibilities and requirements.
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Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Rigorous Programme Evaluation
The role requires leading the development of theories of change and logic models to inform study designs and analytic plans. You must ensure that all evaluation findings are methodologically sound and defensible to effectively guide policy and operational planning.
Business Intelligence and Data Product Oversight
You are expected to supervise the end-to-end delivery of data products, including dashboards, geospatial maps, and machine learning models. This requires balancing technical accuracy with the need to provide actionable insights that challenge assumptions and validate program initiatives.
Strategic Consulting and Stakeholder Management
As a senior leader, you will act as an advisor to internal divisions, particularly regarding outsourced evaluations. You must translate complex quantitative findings into clear, business-oriented recommendations for stakeholders in health or social care settings.
Likely challenges to think through
Navigating Ambiguity in Social Care Data
The role involves handling uncertainty while maintaining a focus on business objectives. Prepare by demonstrating how you have previously structured vague business problems into concrete analytic frameworks, especially when data quality or availability in social care settings is inconsistent.
Balancing Technical Rigor with Tight Deadlines
You will manage multiple projects under pressure. The challenge is maintaining high standards of statistical accuracy while meeting rapid operational demands. Prepare by discussing your project management methodology for prioritizing tasks without compromising the integrity of your analytic outputs.
03
Practice your answers
Technical practice
Designing Programme Evaluations
What to coverExplain your process for developing a theory of change and logic model for a new social care initiative. Discuss how you determine appropriate outcome indicators and ensure the study design is robust enough to inform policy decisions.
Statistical Rigor in Business Intelligence
What to coverHow do you ensure that descriptive statistics and projections provided to stakeholders are defensible and accurate? Describe your approach to validating hypothetical relationships before they are used to justify program initiatives.
Machine Learning for Operational Insights
What to coverProvide an example of how you have applied machine learning methods to solve a business problem. Focus on how you balanced model complexity with the need for interpretability by non-technical operational stakeholders.
Geospatial Data Analysis
What to coverDiscuss your experience with geospatial mapping in a health or social care context. Explain how you use spatial data to identify service gaps or optimize resource allocation for community partners.
Tool Selection and Data Pipeline Management
What to coverWhen given a choice between Python, R, or Stata for a specific evaluation project, what criteria do you use to select the most appropriate tool? Address how you ensure reproducibility and data integrity throughout the extraction and analysis pipeline.
Data Visualization Strategy
What to coverHow do you approach the design of dashboards in Tableau or Power BI to ensure they are actionable for senior leadership? Suggestion: Focus on balancing high-level executive summaries with the ability to perform deep-dive analysis.
Managing Outsourced Evaluations
What to coverAs a consultant to internal divisions, how do you evaluate the quality of an external vendor's methodology? Describe the specific metrics or standards you use to ensure their findings align with organizational objectives.
Handling Data Ambiguity
What to coverDescribe a situation where you had to provide insights based on incomplete or messy data. How did you communicate the limitations of your analysis while still providing actionable recommendations to stakeholders?
Behavioural practice
Leading Through Ambiguity
What to coverDescribe a time when you had to lead a data project with unclear requirements or shifting business objectives. How did you maintain team focus and ensure the final output remained relevant to the organization?
Supervisory Management and Mentorship
What to coverShare an example of how you have mentored junior analysts to improve their technical rigor or business acumen. How do you balance providing guidance with allowing them the autonomy to solve problems independently?
Managing Multiple Stakeholders
What to coverTell me about a time you had to manage competing priorities from different business units under a tight deadline. How did you negotiate timelines and ensure that the most critical analytical needs were met?
Influencing Decision-Making
What to coverGive an example of a time your data analysis challenged a long-held assumption within the organization. How did you present your findings to ensure they were accepted and acted upon by leadership?
02
Sharpen your knowledge
Topics to prepare
Programme Evaluation Frameworks
Review the construction of theories of change and logic models. Exercise: Draft a logic model for a hypothetical social care program, identifying inputs, activities, outputs, and outcomes. This is essential for the role's requirement to guide study designs and outcome indicator selection.
Quantitative Methods for Policy Planning
Refresh knowledge on statistical inference, correlation analysis, and predictive modeling. Exercise: Prepare a summary of how you would validate a program's hypothesis using descriptive statistics versus predictive projections. This aligns with the need to challenge assumptions and inform operational decision-making.
Skills to revise
Data Visualization Strategy
Practice creating interactive dashboards in Tableau or Power BI that translate complex datasets into actionable business insights. Exercise: Design a mock dashboard layout that tracks key performance indicators for a health service, focusing on clarity for non-technical stakeholders.
Supervisory Project Management
Develop a framework for overseeing end-to-end analytics projects while managing tight deadlines and ambiguity. Exercise: Create a project tracking template that balances technical rigor with business objectives, specifically addressing how to supervise outsourced evaluations.
Statistical Programming (Python/R/Stata)
Ensure proficiency in data extraction and machine learning workflows. Exercise: Write a script to automate a routine data cleaning or exploratory analysis task, ensuring the code is documented and reproducible for team use.
04
Your preparation checklist
Prepare a portfolio of programme evaluation case studies
Compile examples of your past work where you developed theories of change and logic models. Be ready to explain how you determined outcome indicators and sample sizes for specific social or health care initiatives.
Develop a technical demonstration of data product lifecycle
Prepare a walkthrough of a data product you led, from initial dataset extraction to the final dashboard or geospatial map. Highlight how you ensured data accuracy and addressed business objectives during the development process.
Refine your leadership and project management narrative
Draft specific examples of your 4+ years of supervisory experience, focusing on how you managed multiple concurrent projects under tight deadlines and guided team members through ambiguous analytical requirements.
Prepare for technical discussions on statistical rigor
Review your proficiency in Python, Stata, or R, and be prepared to discuss how you select specific quantitative methods to challenge assumptions or validate program initiatives in a health or social care context.
Formulate strategic questions for the interview panel
Prepare questions regarding how the data analytics team currently collaborates with internal divisions on outsourced evaluations and how the department balances ad-hoc requests with long-term policy planning goals.
Availability last checked: 19 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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