Understand the new data needs of different teams in Group Risk and propose solution enhancements that will benefit majority of the data users.
Lead and/or participate in key strategic initiatives and focus projects and ensure the implementation is delivered with quality within the stipulated timeframe.
Continuously review the data and operational processes of the data platforms managed by RDSS and propose the necessary enhancements to optimize performance and efficiency.
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As a Project Management Intern within the TRS team, you will support both technical tax reporting processes and project coordination activities. This internship is designed to give you exposure to tax compliance while sharpening your skills in communication, presentation, and project management.
Overall, PwC Malaysia’s Tax Reporting Strategy (TRS) team helps clients manage corporate tax compliance, reporting obligations, and process efficiency. The team leverages technology, project management, and strategic thinking to deliver accurate, timely, and efficient tax reporting solutions.
Advise CIOs, CTOs, CDAOs and CFOs on AI as a technology problem. Tie what the business wants back to the platform, talent and governance decisions that have to come first.
Design the operating model. Where AI capability lives, who runs it, how it's funded, how it moves through the delivery lifecycle, and how it's governed once it's in production.
Set the platform and cloud readiness strategy that makes enterprise AI possible. You won't build it. You'll decide what good looks like and hold the line on it.
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Support the team in driving end-to-end growth initiatives across assortment, traffic, conversion, and seller performance through data analysis and structured problem solving.
Assist in strategic projects and growth experiments, including promotional mechanics, subsidy programs, flash sales, and other platform initiatives to improve business performance.
Analyze user behavior, marketplace trends, competitor activities, and business performance to identify growth opportunities and generate actionable insights.
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Support the team in driving end-to-end growth initiatives across assortment, traffic, conversion, and seller performance through data analysis and structured problem solving.
Assist in strategic projects and growth experiments, including promotional mechanics, subsidy programs, flash sales, and other platform initiatives to improve business performance.
Analyze user behavior, marketplace trends, competitor activities, and business performance to identify growth opportunities and generate actionable insights.
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If you are looking to excel and make a difference, take a closer look at us…
We seek to strike a balance between diversity, inclusion and merit to achieve our mission of infusing diversity in thinking and skillsets into our organisation. Candidates are assessed based on merit and potential, in line with our mission to attract and recruit the best talent available. Expanding on our “Digital at the Core” ethos, we are progressively digitising the employee journey and experience to provide a strong foundation for our people to drive life-long learning, achieve their career aspirations and grow talent from within our organisation.
Participate in defining and continuously improving data quality standards, judgment rules, and acceptance criteria for large model training and evaluation scenarios.
Review data outputs against quality standards, identify issues, drive correction loops, and ensure accuracy and consistency.
Assess complex, ambiguous, and boundary cases, and turn decisions into reusable judgment rules and knowledge assets.
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Participate in defining and continuously improving data quality standards, judgment rules, and acceptance criteria for large model training and evaluation scenarios.
Review data outputs against quality standards, identify issues, drive correction loops, and ensure accuracy and consistency.
Assess complex, ambiguous, and boundary cases, and turn decisions into reusable judgment rules and knowledge assets.
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Participate in defining and continuously improving data quality standards, judgment rules, and acceptance criteria for large model training and evaluation scenarios.
Review data outputs against quality standards, identify issues, drive correction loops, and ensure accuracy and consistency.
Assess complex, ambiguous, and boundary cases, and turn decisions into reusable judgment rules and knowledge assets.
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