New opportunity

Data & AI Project Manager

WSA APAC · Singapore

About this role

WSA APAC is seeking a Data & AI Project Manager for its AI Center of Excellence in Singapore. The role coordinates people, technology and business priorities to deliver Data and AI initiatives across the organization and turn them into business outcomes in the hearing-care sector.

Responsibilities

  • Lead projects from planning through implementation and adoption, aligning them with wider business and IT programs.
  • Translate priorities into project plans, milestones, deliverables, timelines and dependencies. Coordinate AI specialists, data teams, IT functions, business stakeholders and external partners across interconnected workstreams.
  • Manage budgets, resources, risks, issues, dependencies and stakeholder communications. Integrate Data and AI deliverables into broader program plans, support business adoption, and communicate progress, expectations and outcomes across functions and regions.

Requirements

  • At least 5 years of project or program management experience in IT, technology, data, digital transformation or a related environment, including complex, cross-functional initiatives with multiple teams and stakeholders.
  • Strong planning, organization, coordination, stakeholder management and communication skills; ability to manage competing priorities and deadlines and collaborate across cultures and regions. A pragmatic, collaborative, delivery-oriented approach and strong problem-solving skills are expected.

Preferred

Experience with Data, AI, Generative AI or digital transformation initiatives, alongside a foundational understanding of large language models, AI agents and retrieval-augmented generation (RAG).

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Project managementProgram managementProject planningProject governanceBudget managementResource managementRisk managementStakeholder managementCross-functional coordinationCommunicationProblem-solvingData and AI initiativesDigital transformationGenerative AILarge language modelsAI agentsRetrieval-augmented generation

Your skill match

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Get interview-ready for this role

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

  • Turn priorities into executable plans

    The posting calls for translating business and technology priorities into milestones, deliverables, timelines and dependencies. Prepare to explain how you would make a complex Data or AI initiative trackable without losing sight of its business purpose.

  • Coordinate interdependent delivery

    The role aligns AI specialists, data teams, IT, business stakeholders and external partners, and integrates their deliverables into wider programs. Prepare an example of how you have managed handoffs and kept parallel workstreams moving.

  • Maintain project governance

    The manager is responsible for budgets, resources, risks, issues, dependencies and stakeholder communication. Prepare to show how you make trade-offs visible and escalate decisions when priorities or deadlines conflict.

  • Carry delivery through adoption

    The posting covers planning through implementation and business adoption, with an emphasis on meaningful outcomes. Prepare to distinguish a completed technical deliverable from one that business teams can use.

Likely challenges to think through

  • Balancing linked workstreams

    Preparation hypothesis: A delay in one team’s deliverable could affect a wider business or IT program. The trade-off is between preserving the overall timeline and changing scope or sequencing. Prepare an example of mapping dependencies, presenting options and agreeing a revised plan.

  • Keeping technical and business expectations aligned

    Preparation hypothesis: AI specialists and business stakeholders may judge progress differently. The difficulty is communicating technical uncertainty without obscuring delivery decisions. Prepare a plain-language explanation of a Data or AI project’s milestones, risks and expected outcome; foundational knowledge of LLMs, agents and RAG is preferred in the posting.

  • Moving from implementation to use

    Preparation hypothesis: Delivery may be considered complete before teams adopt the result. The trade-off is between closing the project and allowing time for adoption. Prepare to explain how you would identify adoption dependencies, communicate expectations across functions and regions, and track whether the intended outcome is being reached.

03

Practice your answers

Technical practice

How would you align a Data or AI project with broader business and IT programs?

What to coverSuggested preparation: Explain how you would establish the intended business outcome, identify related programs and agree on decision-makers. Address how you would handle a conflict between a project goal and a wider program priority.

How would you turn business and technology priorities into a delivery plan?

What to coverSuggested preparation: Describe how you would define deliverables, milestones, timelines and owners with the relevant teams. Explain how you would make assumptions and dependencies visible before committing to dates.

How would you manage dependencies between parallel workstreams?

What to coverSuggested preparation: Discuss how you would track handoffs and sequence Data and AI deliverables within a wider program plan. Cover what you would do when one workstream slips and affects others.

How would you coordinate AI specialists, data teams, IT, business stakeholders and external partners?

What to coverSuggested preparation: Explain how you would clarify responsibilities, decisions and handoffs without assuming every team works the same way. Address how you would resolve an ownership gap that stalls delivery.

How would you manage a project budget and limited resources?

What to coverSuggested preparation: Describe how you would compare planned and actual use of budget and people against upcoming milestones. Explain the trade-offs you would present if capacity could not support every requested deliverable.

How would you distinguish and manage a project risk, issue and dependency?

What to coverSuggested preparation: Define each in practical delivery terms, then explain how you would assign an owner, track a response and escalate when needed. Keep the example tied to a cross-functional Data or AI initiative rather than an assumed WSA project.

How would you report progress and delivery expectations across functions and regions?

What to coverSuggested preparation: Explain how you would communicate milestones, decisions, risks and changes in expectations to audiences with different needs. Address how you would report uncertainty without obscuring its effect on delivery.

How would you plan for business adoption after implementation?

What to coverSuggested preparation: Describe how you would involve business stakeholders before release and establish what they need to use the deliverable. Explain how feedback after implementation could inform the remaining project plan.

How would you evaluate whether a Data or AI initiative achieved a business outcome?

What to coverSuggested preparation: Propose agreeing on an intended outcome and a practical measure with stakeholders at the start, then comparing results with that baseline. Distinguish completing a deliverable from its adoption and business effect; these are suggested evaluation approaches, not stated WSA measures.

What should a project manager understand about LLMs, AI agents and RAG?

What to coverSuggested preparation: Explain these concepts at a foundational level and how their differences could affect planning discussions with AI specialists. For example, consider what information a RAG approach needs and what additional steps an agent might take, without claiming the role requires designing either system.

Behavioural practice

Tell me about a time competing priorities threatened a deadline.

What to coverSuggested preparation: Give the situation and the competing demands, then describe how you agreed on a practical priority or trade-off. State the result, including any change to scope or timing.

Tell me about a time you coordinated stakeholders across functions or regions.

What to coverSuggested preparation: Set out the situation and why the groups needed to work together. Describe your communication and coordination actions, then explain the effect on decisions or delivery.

Tell me about a time you resolved a problem between interdependent teams.

What to coverSuggested preparation: Describe the delivery blockage and each team's role without assigning blame. Explain how you established ownership or a workable handoff, and what happened afterward.

Tell me about a time you helped people adopt a delivered change.

What to coverSuggested preparation: Explain the situation, who needed to use the change and what stood in their way. Describe your actions with stakeholders and the observable result, distinguishing launch from actual use.

Tell me about a time you had to communicate an emerging delivery risk.

What to coverSuggested preparation: Describe when you identified the risk, whom it affected and how you communicated its likely impact and options. Explain the decision or action that followed and the outcome.

02

Sharpen your knowledge

Topics to prepare

  • Project lifecycle and business alignment

    The role covers Data and AI projects from planning through implementation and adoption, aligned with wider business and IT programs. Revise by explaining what decisions and deliverables belong at each stage, and how you would check that the intended business outcome remains clear.

  • Interdependent workstreams

    The posting emphasizes parallel workstreams involving AI specialists, data teams, IT, business stakeholders and external partners. Draw a hypothetical dependency map and explain how a delay in one workstream would affect milestones in another.

  • Project governance and trade-offs

    Budgets, resources, risks, issues and dependencies are explicit responsibilities. Revise by comparing how you would respond to a risk that may occur, an issue already affecting delivery and a resource conflict between priorities.

  • Foundational AI concepts

    Experience with Data, AI, Generative AI or digital transformation initiatives and foundational understanding of LLMs, AI agents and RAG are preferred. As preparation, explain each concept in plain language and identify questions a project manager would ask specialists about scope, dependencies and adoption; the posting does not call for building these systems.

Skills to revise

  • Build an integrated project plan

    Practise turning hypothetical business and technology priorities into milestones, deliverables, timelines, owners and dependencies. This directly reflects the planning and wider-program integration described in the posting.

  • Maintain a governance view

    Create a concise sample status view covering budget, resource constraints, risks, active issues, dependencies and decisions needed. The role explicitly manages these areas and communicates delivery expectations.

  • Coordinate a cross-functional decision

    Role-play a meeting in which AI, data, IT and business workstreams have competing deadlines. Practise clarifying the dependency, agreeing an owner and next step, and recording the impact on the plan; cross-functional coordination and problem-solving are explicit requirements.

  • Communicate progress and adoption

    Draft two short updates on the same hypothetical initiative: one for a delivery team and one for business stakeholders in another region. Include progress, a risk, the decision needed and an adoption consideration. The posting calls for clear communication across functions and regions and support for business adoption.

04

Your preparation checklist

  • Map evidence to the role

    The posting asks for 5+ years of relevant project or program management and complex, cross-functional delivery. Prepare a brief evidence sheet with dates, scope, teams, workstreams and your responsibilities from applicable examples; leave gaps explicit.

  • Rehearse a project walkthrough

    Choose a relevant technology, data or AI initiative if you have one. Explain how you moved it from planning through implementation and adoption, handled competing priorities, and measured outcomes; distinguish your decisions from the team’s work.

  • Draft a sample delivery plan

    As interview practice, turn a hypothetical Data and AI priority into milestones, deliverables, owners, timelines and dependencies. Show where it would connect to wider business and IT programs; do not present the exercise as a WSA project.

  • Prepare a governance example

    Bring a concise example or anonymized artifact showing how you tracked budgets, resources, risks, issues and dependencies, escalated a trade-off, and communicated a decision. These are stated responsibilities of the role.

  • Practice cross-functional updates

    Draft a short status update for AI specialists, data teams, IT, business stakeholders and external partners. Include progress, delivery expectations, risks and next decisions, then explain how you would adapt it across functions and regions.

  • Review preferred AI concepts and prepare questions

    The posting prefers experience with Data, AI, Generative AI or digital transformation and foundational knowledge of LLMs, AI agents and RAG. Prepare plain-language explanations and ask WSA how its AI Center of Excellence sets priorities, integrates deliverables into wider programs and assesses business adoption.

AI-generated from the job information · Prepared Oct 1, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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