Monitor & measure the performance & availability of systems proactively; implement corrective actions identified to improve performance & service level availability.
Respond promptly to incident, root cause analysis & provide temporary and/or permanent resolution of incidents escalated. Provide timely status updates to relevant parties.
Implement service continuity measures, i.e., backup/restore procedures & disaster recovery plan, to ensure continuous operation of the business.
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Write, run, and maintain basic scripts (e.g., SQL queries, data validation scripts, automation utilities) to support configuration, testing, and data migration activities
Support system integrations by assisting with interface testing, data mapping validation, and monitoring batch or API executions.
Assist with data migration activities, including data extraction, transformation support, data loading, and reconciliation checks.
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IT Transformation: Shaping client’s major IT Transformation program and running the Transformation Office to deliver business and technology initiatives.
IT M&A: Working on large international M&A deals (mergers, acquisitions, carve outs etc.) covering due diligence, day 1 strategy, planning, operating model, architecture, and post day 1 optimization and value delivery.
IT Sourcing: working with clients to optimize their service delivery models, identifying and executing sourcing options for them.
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Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
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Manage and monitor PTP process with proper controls and high attention to detail to ensure complete, accurate and timely payment processing to vendors, providing accurate and timely management information and reconciliations as required.
Liaise with internal/external stakeholders as and when necessary
Organize and coordinate Service Review Meeting
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Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.
Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.
Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.
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Supporting stakeholders to clarify requirements, articulate problem statements, assess dependencies and constraints, and align priorities among diverse groups.
Provide hands-on support in technical discussions related to configurations, customizations, integrations, and data migration strategy.
Lead configuration, parameterization, and functional system testing efforts.
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Responsible for the development of deep learning and operations research models and related intelligent systems for the supply chain and logistics of the global E-Commerce business.
Utilize e-commerce big data and deep learning models to predict end-to-end estimated time of arrival (ETA), and some logistics events such as failed delivery, delivered but not received to enhance the user logistics experience. Build logistics network knowledge graphs and predict the spatio-temporal trajectory sequence of express packages through deep learning, statistical inference and other algorithmic methods. Use NLP and LLM algorithms to handle address problems such as address verification and address suggestion.
Utilize time series forecasting techniques to predict sales at different granularities and horizons, such as warehouse-level manpower forecasting, inventory-level demand forecasting etc. We need strong machine learning and deep learning skills to detect important factors and model the relationship between the future and history.
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