New opportunity

Product Manager - AI Transformation, Finance

Shopee · Singapore

About this role

Shopee’s Enterprise Efficiency team is seeking a Product Manager to lead AI transformation of finance workflows across Sea’s global markets. The role focuses on AI-powered platforms for accounts payable, invoicing, reconciliation and financial reporting.

Responsibilities

Define the finance AI transformation roadmap, identify high-impact automation opportunities and work with global finance teams to understand pain points and redesign processes.
Own the product lifecycle from scoping and product requirements documents through launch and iteration. Products include intelligent invoice review, automated accounts payable processing, smart reconciliation and AI-assisted reporting.
Collaborate with AI engineers to design, evaluate and improve agents and models for finance use cases. Turn model capabilities and limitations into product decisions, including human-in-the-loop processes and exception handling.
Set and track success measures such as automation rates, accuracy and SLA improvements; use the results to improve products and support further AI investment.
Coordinate finance stakeholders and AI engineering, platform engineering, design and QA teams. Manage multi-region rollouts and change management as workflows evolve.

Requirements

Product management experience, including internships. Strong logical and structured thinking, with the ability to turn complex, ambiguous finance processes into clear product requirements and AI solution designs.
Excellent communication and cross-functional collaboration with finance users and technical teams. Self-direction, ownership, comfort with ambiguity and a willingness to learn.

Advantageous qualifications: A bachelor’s degree in information systems, computer science or technology, or equivalent professional experience. Prior exposure to AI, automation, fintech or finance operations is a plus. Familiarity with LLMs, AI agents or automation tools is a strong plus, though enthusiasm and willingness to learn matter more.

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

Skills for this role

Product ManagementAI agentsAI automationLarge Language ModelsFinance operationsAccounts payableInvoicingReconciliationFinancial reportingProduct requirements documentsHuman-in-the-loop designException handlingData analysisStakeholder managementCross-functional collaborationChange management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Prioritize finance automation
The role drives an AI roadmap across accounts payable, invoicing, reconciliation and reporting. Focus on how to identify high-impact opportunities with global finance teams and turn pain points into a sequenced roadmap.
Take products from requirements to iteration
The posting assigns ownership from scoping and PRDs through launch and improvement, including invoice review, AP processing, reconciliation and reporting products. Prepare to explain how an ambiguous workflow becomes a clear product requirement.
Make practical AI product decisions
Work with AI engineers to evaluate agents and models, then account for their limitations through human-in-the-loop design and exception handling. The emphasis is on product judgment, not assuming every step can be automated.
Measure outcomes and support investment decisions
Track automation rates, accuracy and SLA improvements; use the results to guide iterations and build the case for further AI investment.
Coordinate adoption across regions
Bridge finance users with AI engineering, platform engineering, design and QA while managing multi-region rollout and change management as workflows evolve.

Likely challenges to think through

Standardization versus local workflow needs
Likely preparation challenge, not a stated employer problem: a roadmap spanning global finance teams may need to balance reusable products against differences in how teams work. Prepare an approach for mapping a workflow, separating common steps from local exceptions, and deciding what to roll out first.
Automation versus review effort
Likely preparation challenge: increasing automation without weakening accuracy or overwhelming reviewers requires choices about when AI should hand work to a person. Prepare to discuss how model evaluation, exception handling and human review would shape a launch and later iterations.
Speed of rollout versus demonstrated value
Likely preparation challenge: deploying across regions may compete with the time needed to validate accuracy, SLA changes and adoption. Prepare a phased measurement and feedback approach that uses results to decide whether to iterate or expand.

Practice your answers

Technical practice

How would you prioritize AI opportunities across accounts payable, invoicing, reconciliation and reporting?

What to coverSuggested preparation: Compare workflow pain, manual effort, expected impact and feasibility before proposing a roadmap. Explain what you would validate with finance users rather than assuming every process should be automated.

How would you investigate an ambiguous finance workflow before redesigning it with AI?

What to coverSuggested preparation: Describe how you would map the current steps, users, handoffs and exceptions with finance teams. Distinguish the underlying problem from requests to automate an existing step.

What would you put in a PRD for intelligent invoice review?

What to coverSuggested preparation: Cover the user problem, workflow, expected outputs, acceptance criteria and handling of uncertain results. Explain how you would make the requirements clear enough for finance, engineering, design and QA to use.

How would you scope automated accounts payable processing?

What to coverSuggested preparation: Identify which steps might be suitable for automation and which need a person to review or resolve an exception. Discuss the trade-off between processing speed and the accuracy required for finance work.

How would you design a smart reconciliation workflow when records do not match?

What to coverSuggested preparation: Explain how you would distinguish straightforward matches from discrepancies needing investigation. Describe the information a finance user would need to understand and resolve an exception.

How would you assess an AI-assisted financial reporting feature?

What to coverSuggested preparation: Define the reporting task and how a user would check whether an AI-generated result is accurate and useful. Consider where review should occur before the output becomes part of a finance workflow.

How would you evaluate an AI agent or model for a finance use case?

What to coverSuggested preparation: Propose representative test cases, including routine inputs and exceptions, and compare results against agreed expectations with finance users and AI engineers. Explain how observed limitations would affect the product scope or launch decision.

When should an AI-powered finance workflow hand work to a human?

What to coverSuggested preparation: Discuss how uncertain outputs and exceptions could be routed for review, with enough context for a person to act. Explain the trade-off between a higher automation rate and dependable outcomes.

How would you measure whether a finance AI product is improving?

What to coverSuggested preparation: Use the posting’s measures of automation rate, accuracy and SLA improvement, and define what each would mean for a specific workflow. Explain how you would use the results to prioritize iterations and assess the case for further investment.

How would you plan a rollout of an AI finance workflow across multiple regions?

What to coverSuggested preparation: Identify which workflow assumptions you would check with regional finance teams before rollout and how you would gather feedback after launch. Discuss how product iteration and changes to team workflows might be coordinated.

Behavioural practice

Tell me about a time you turned an ambiguous problem into clear product requirements.

What to coverSuggested preparation: Use a real example, including an internship if relevant, and set out the situation, the questions you asked and the requirements you produced. Finish with the result and what you learned about resolving ambiguity.

Tell me about a time you helped business and technical stakeholders understand each other.

What to coverSuggested preparation: Describe the situation, the differing needs or terminology and the actions you took to clarify a decision. State the result without implying you have worked with finance or AI teams if you have not.

Tell me about a time you had to prioritize competing requests.

What to coverSuggested preparation: Explain the situation, your decision criteria and how you communicated the trade-offs. Give the outcome and any adjustment you made after receiving feedback.

Tell me about a time you supported a change to an established workflow.

What to coverSuggested preparation: Give the situation, how you listened to affected users and what you did to help them adopt the change. Describe the result, including any resistance or unresolved issue you learned from.

Tell me about a time you learned an unfamiliar domain or technology to move work forward.

What to coverSuggested preparation: Choose a real situation and describe how you identified what you needed to learn, sought input and applied it. End with the result and how you checked your understanding rather than overstating expertise.

Sharpen your knowledge

Topics to prepare

Finance workflow fundamentals
Posting focus: accounts payable, invoicing, reconciliation and financial reporting. Suggested revision: sketch how an invoice moves through review, payment processing and reconciliation; mark where manual work or exceptions could arise. Use the sketch to explain where automation might help.
AI agents and human oversight
Posting focus: working with AI engineers on agents and models, while accounting for limitations, human-in-the-loop design and exception handling. Familiarity with LLMs and agents is a plus, not a stated requirement. Suggested revision: compare an AI-assisted invoice review with a fully automated one and identify decisions that should go to a person.
Transformation measures and trade-offs
Posting focus: tracking automation rates, accuracy and SLA improvements to guide iteration and support an investment case. Suggested revision: define each measure for a hypothetical invoice-review workflow and explain how you would respond if automation rose but accuracy fell.
Multi-region rollout and change management
Posting focus: coordinating finance and technical teams across regions as workflows change. Suggested revision: outline the questions you would ask regional finance users before rollout and the feedback you would monitor afterward.

Skills to revise

Turn an ambiguous process into a PRD
Posting focus: structured thinking, clear requirements and ownership from scoping through iteration. Suggested practice: draft a short PRD for intelligent invoice review with the user pain point, workflow, AI-assisted steps, human review points, exception paths and success measures.
Prioritize automation opportunities
Posting focus: defining a finance AI roadmap and finding high-impact opportunities. Suggested practice: compare invoice review, AP processing and reconciliation using stated assumptions about manual effort, potential benefit and implementation uncertainty; explain your proposed order without presenting it as Shopee’s plan.
Evaluate an AI-assisted workflow
Posting focus: collaborating with AI engineers to evaluate and improve finance agents and models. Suggested practice: make a small hypothetical set of invoice cases, including ambiguous ones, and specify what counts as a correct outcome, an exception and a case for human review.
Communicate a rollout decision
Posting focus: bridging finance users with AI engineering, platform engineering, design and QA. Suggested practice: give a brief explanation of a proposed workflow change for finance users, then restate its requirements, test cases and unresolved risks for the technical teams.

Your preparation checklist

Map a finance workflow
The role covers accounts payable, invoicing, reconciliation and reporting. Prepare a one-page map of one workflow, showing pain points and where AI might help; label any assumptions.
Draft a prioritised roadmap
The role calls for identifying high-impact automation opportunities. Prepare a short comparison of two opportunities, with your proposed impact, effort and risks clearly marked as estimates.
Write a sample product requirements document
Choose intelligent invoice review or smart reconciliation, both named in the posting. Draft the problem, users, scope, workflow, acceptance criteria and open questions.
Design for AI limitations
The posting explicitly mentions model evaluation, human-in-the-loop design and exception handling. Sketch how outputs would be checked, when a case would go to a finance user and how errors would inform iteration.
Define a measurement plan
Prepare proposed definitions for automation rate, accuracy and SLA improvement—the measures named in the posting—plus a simple way to compare results before and after launch.
Prepare a project walkthrough
Select a real product-management example, including an internship if relevant, and outline your role, how you clarified ambiguity, collaborated across teams and used results. Do not imply finance or AI experience you do not have.
Prepare rollout questions
The role spans global finance teams and multi-region change management. Ask how workflows vary by region, who reviews AI exceptions and what evidence would support a wider rollout.

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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