Applied Scientist Ii

Payroll Tech's Sheriff team develops and maintains ML and Generative AI applications that support Payroll Operations and Amazon employees at scale. Our portfolio includes Pay-Input Anomaly Detection, which improves the pay experience by identifying pay input irregularities such as leaves and insurance discrepancies; Percept, which improves ticket resolution by providing intelligent ticket prioritization via sentiment scoring, ticket summarization, defect classification, and categorization; Penny, a Virtual Assistant that enables payroll operations teams to efficiently retrieve information from multiple sources including policies, Percept data, vendor data, and HR data via Xylem through a single browser interface; Pay Ticket Genie, in process of being integrated with Amazon AZA(A to Z Assistant); Niyam, our rule engine; and Policy as Code Extraction (PoCo), a critical component of SPACE (Single Payroll Autonomous and Computation Engine) Amazon's in-house payroll system built to eliminate third-party vendor dependency for payroll processing. PoCo ensures data accuracy by validating that pay instructions are correct and performing calculations when required. It consists of two components: policy-based rule creation, where business owners select a pay code and provide policy links to generate rules for specific business processes, and rule evaluation, where upstream services send real-time validation or calculation requests and receive results along with rationale for any failures. Sheriff team owns policy-based rule creation and powering the rule evaluation system with rules generated.