Microsoft Data Analytics 

Taiwan, Taoyuan City 

479916080

Central Fraud and Abuse Risk (CFAR)awho will help usOur goal is to minimizefinancial and reputationalharm by preventing, detecting, and addressing fraud.

As a CFARocus onunderstanding andharnessing diverse data sources, conducting thorough analyses, and deriving actionableand visually intuitivethe power of generative AI and the actionorientation of agentic AI

Required/Minimum Qualifications:

• Bachelor's Degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics or related field AND 2+years experiencein data analysis and reporting, data science, business intelligence, or business and financial analysis
  • ORMaster's Degree in Mathematics, Analytics, Data Science, Engineering, Computer Science, Business,Economicsor related field.
  • OR equivalent experience.

Other Requirements:

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings:

• : This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred qualifications:

• Experience working with cloud computing platforms like Azure, AWS.

• Microsoft Certified: Fabric Analytics Engineer Associate.

• Experience usinggenerative AI tools, configuring AIagentsand presenting findings.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• appropriateanalyticaltechniques, conduct analyses, and provide actionable recommendations to address business questionsat scale,usingthelatestavailable tools
• Collaborate across teams to ensure consistency in data sources, methods, models, tools, and business priorities, to build reusable and sharableimprovementtechniques
• Developexperiments andprototypesusinga deep understanding business process and underlying data structures todrivemeaningful recommendationsfor actionstoCFAR’s fraud reduction remit
• Translate theworkofourdata scientists’custommachine learningmodelsand generative AIintointuitive, context-sensitiveinsights forinternal stakeholderdecision-making
• Analyzeandimplementnew AI toolsandalgorithmsto help simplifyfraud patternrecognitionand human understandingtoenableCFAR solutionsthatbetteraddressfraud
• Staycurrent onMicrosoftdata privacyand securitystandardsas wellas regulatoryand ethicalrequirementstoensureproper data handling andcompliant analytic