Agentic Recommendation System: Explore the application of LLMs in data analytics, covering user behavior analysis, crowd segmentation, customer acquisition analysis, growth analytics and profitability analysis, and advance the implementation of the new paradigm of Agentic Data Analysis.
AI for Data Science: Build AI-powered capabilities for data science teams. Leverage LLMs and Agents to construct automated tools for anomaly attribution, experiment analysis and insight generation; accumulate reusable data science toolkits and standardized analysis frameworks, and build an AI-driven data science team.
Currently pursuing a Bachelor's degree or above in quantitative analysis-related disciplines, including Mathematics, Statistics, Operations Research, Data Science, Computer Science, Economics and Business Analytics.
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Provide ad hoc support to the team as needed to improve operational efficiency.
Currently, in the final year of pursuing a degree in Accounting, Audit, Business, Risk Management, Data Analytics, Computer Science, or related fields.
Proficiency in analytical skills and ability to work independently and collaboratively in a fast-paced, global environment.
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Support the development and ongoing enhancement of policies and procedures across the transaction monitoring lifecycle, including rule validation, system optimization, and underlying data analysis.
Coordinate with regional and global teams to support the implementation and refinement of transaction monitoring process.
Partner with cross-functional stakeholders within Global Payments to ensure operational efficiency and clearly defined responsibilities.
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Optimize system performance across latency, throughput, and cost dimensions by improving prompt chains and implementing scalable caching strategies for AI features serving large user bases.
Develop evaluation frameworks and guardrails, including LLM-as-a-judge and human-in-the-loop approaches, to ensure outputs are safe, accurate, and reliable, especially for compliance-sensitive industries such as finance. Evaluate multiple models on common tasks to identify trade-offs and inform go-to-market decisions.
Collaborate closely with product and algorithm research teams to feed real-world customer signals back into the product roadmap and model development process.
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Individuals who are completing or have recently completed a PhD degree in Electrical Engineering, Thermal Engineering, Energy & Power, Materials, Refrigeration, Environmental Engineering, Mechanical Engineering, or related fields are preferred.
Possess excellent problem analysis and solving skills, an innovative and rigorous mindset, and the ability to independently overcome technical difficulties.
Possess independent research capabilities for theoretical and applied research. Familiar with liquid cooling, thermal management, hydrophilic coatings, renewable energy, and energy storage, keeping track of cutting-edge industry technologies.
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Travel may be required up to 15% - 30% of the time.
Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science, Information Science, Engineering, Mathematics or a related discipline.
Good understanding of network protocols including TCP/IP, DHCP, BGP, OSPF/IS-IS and MPLS related technologies.
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