[LTA-VS] ASSISTANT MANAGER, STATISTICS & SERVICE SYSTEMS DEVELOPMENT
LTA Land Transport Authority · Singapore
About this role
The Assistant Manager, Statistics & Service Systems Development will join a team focused on designing and developing automation and AI solutions to transform vehicle-related data services at the Land Transport Authority (LTA). The role involves building robotic process automation (RPA) workflows to streamline business processes, developing and deploying AI-enabled tools, and creating intuitive dashboards and data products for stakeholders. You will work with rich datasets on vehicle-related statistics to translate complex data into clear, visually compelling insights that support policy-making and public-facing publications. Responsibilities include partnering with leadership and stakeholders to identify automation opportunities, prototyping solutions, and driving the adoption of digital tools across the division. Requirements include strong analytical skills, a meticulous approach to complex datasets, and the ability to work independently in a fast-paced environment. Candidates should possess strong communication and interpersonal skills to present ideas clearly to diverse stakeholders. A self-directed learning mindset is essential to keep pace with emerging AI and automation technologies. Preferred qualifications include a background in Computer Science, Information Technology, Data Science/Analytics, or Design/Human-Computer Interaction. Hands-on experience with RPA solutions (e.g., UiPath, Power Automate) and AI tools, including prompt engineering or machine learning applications, is highly valued. Proficiency in data visualization tools like Power BI or Tableau, along with familiarity with Python, SQL, and relational databases (e.g., IBM Query Management Facility), is considered an advantage.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Automation and AI Solution Development
The core mandate is to design and deploy RPA workflows and AI-enabled tools to streamline business processes. You must demonstrate the ability to identify operational bottlenecks, prototype effective digital solutions, and drive their adoption across the division.
Data Visualization and Insight Communication
You are responsible for transforming complex vehicle-related datasets into intuitive, visually compelling dashboards and reports. Success requires balancing technical data accuracy with a strong design sensibility to ensure insights are accessible to non-technical stakeholders and suitable for public-facing publications.
Stakeholder Engagement and Policy Support
You must partner closely with leadership and diverse stakeholders to translate data into actionable insights that support government policy-making. This requires clear communication skills to present technical findings and advocate for the value of new digital tools.
Likely challenges to think through
Balancing Technical Complexity with User Accessibility
A significant challenge is simplifying highly complex vehicle statistics for public consumption without losing analytical integrity. Prepare by showcasing examples where you have translated dense data into user-friendly visual formats, emphasizing your design process for non-technical audiences.
Rapid Adaptation to Emerging Technologies
The role requires keeping pace with fast-evolving AI and automation tools. You may face difficulty in maintaining current expertise while managing ongoing projects. Prepare by highlighting your self-directed learning strategies and how you have successfully integrated new tools into existing workflows.
Navigating Diverse Stakeholder Requirements
Working with both internal leadership and external stakeholders often involves conflicting priorities or varying levels of data literacy. Prepare by discussing your approach to gathering requirements, managing expectations, and building consensus when proposing new automation initiatives.
03
Practice your answers
Technical practice
Designing Effective RPA Workflows
What to coverExplain your process for identifying manual business processes suitable for automation. Discuss how you ensure reliability and error handling when building workflows in tools like UiPath or Power Automate.
Data Visualization for Non-Technical Stakeholders
What to coverDescribe your approach to designing dashboards that translate complex vehicle statistics into actionable insights. Focus on how you balance aesthetic design with clarity to ensure non-technical users can interpret the data correctly.
Prompt Engineering and AI Integration
What to coverHow do you approach prompt engineering to improve the accuracy and utility of AI-enabled tools? Provide an example of how you have used AI to assist in workflow design or data analysis tasks.
SQL and Database Management
What to coverDiscuss your experience querying relational databases, such as IBM Query Management Facility or similar systems. Explain how you ensure data integrity and efficiency when extracting large datasets for analysis.
Data Cleaning and Meticulous Analysis
What to coverWhen working with large, complex datasets, what steps do you take to validate data quality before analysis? Describe how you handle missing values or inconsistencies to ensure your final reports are accurate.
Python for Data Automation
What to coverHow have you utilized Python to automate data processing or analysis tasks? Suggestion: Focus on libraries or scripts you have used to manipulate datasets or integrate with other software tools.
Prototyping Digital Solutions
What to coverDescribe your methodology for prototyping new automation or AI tools. How do you gather feedback from stakeholders during the prototype phase to refine the final product?
Machine Learning Applications
What to coverDiscuss a scenario where you applied machine learning concepts to solve a business problem. Focus on the selection of the model and how you evaluated its performance against the original objective.
Behavioural practice
Collaborating with Diverse Stakeholders
What to coverDescribe a time you had to explain a technical automation solution to a non-technical stakeholder. How did you ensure they understood the value and functionality of the tool?
Adapting to Fast-Paced Environments
What to coverGive an example of a project where priorities shifted rapidly. How did you manage your workload and maintain the quality of your deliverables under pressure?
Self-Directed Learning
What to coverThe role requires keeping pace with emerging AI technologies. Describe a recent technology or tool you learned independently and how you applied that knowledge to a project.
Driving Adoption of New Tools
What to coverTell me about a time you introduced a new digital tool or process to a team. What challenges did you face in gaining buy-in, and how did you overcome them?
02
Sharpen your knowledge
Topics to prepare
Data Storytelling for Policy Support
Focus on translating complex vehicle-related datasets into clear, actionable insights for non-technical stakeholders. Exercise: Take a public dataset, identify a key trend, and draft a summary report that explains the policy implications without using technical jargon.
Automation Workflow Design
Understand the lifecycle of identifying, prototyping, and deploying RPA or AI-assisted workflows. Exercise: Map out a manual, repetitive administrative process you have encountered and design a flowchart showing how it could be automated to improve efficiency.
Human-Computer Interaction (HCI) Principles
Review design principles for creating intuitive dashboards that prioritize user experience. Exercise: Evaluate an existing dashboard for clutter and cognitive load, then propose three specific design changes to improve its usability for a general audience.
Skills to revise
RPA Development (UiPath/Power Automate)
Demonstrate hands-on ability to build automated workflows. Exercise: Create a simple automation script that extracts data from a structured file and populates a report, documenting the logic used to handle potential errors.
Data Visualization (Power BI/Tableau)
Proficiency in crafting clean, visually compelling data products. Exercise: Build a dashboard using a sample dataset that includes at least three different chart types, ensuring the layout is clean and the key insights are immediately visible.
Prompt Engineering
Develop techniques to effectively guide AI tools to produce desired outputs. Exercise: Write a series of structured prompts to refine an AI-generated summary of a technical document, testing how different constraints affect the quality and accuracy of the output.
04
Your preparation checklist
Prepare a portfolio of data-driven dashboards
Create a sample dashboard using Power BI or Tableau that transforms a complex dataset into actionable insights for a non-technical audience. Focus on clean design and clear storytelling, as this is a core requirement for LTA's public-facing publications.
Document an automation project walkthrough
Prepare a case study of an RPA workflow you have built using tools like UiPath or Power Automate. Clearly explain the business problem, the technical implementation, and the measurable efficiency gains achieved through the automation.
Demonstrate AI and prompt engineering proficiency
Prepare examples of how you have utilized AI tools or prompt engineering to improve workflow efficiency or data analysis. Be ready to discuss how you stay updated with emerging AI technologies and apply them to solve practical business challenges.
Review technical proficiency in SQL and Python
Brush up on your ability to query relational databases and perform data manipulation using Python. Be prepared to explain how you would handle large, complex datasets typical of vehicle-related statistics.
Formulate questions for the interview panel
Prepare thoughtful questions regarding the team's current priorities for digital transformation, the types of stakeholders you will support, and how the division measures the success of new automation and AI-enabled tools.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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