Own the full fraud lifecycle by designing AI-enabled detection strategies and optimizing fraud rules to minimize risk and friction. Translate complex analytical findings into executive narratives and manage daily fraud review operations and escalations.
GOAT Group
8 Remote Job Openings at GOAT Group
Establish best analytical practices and scale data infrastructure using Snowflake, dbt, and Mode/Metabase. Collaborate with stakeholders to create data models and ensure data quality and governance across various business units.
Analyze marketplace performance data using SQL to uncover insights on liquidity, pricing, and sell-through. Develop SQL-based dashboards and optimize workflows using AI tools to support supply growth and strategic initiatives.
Manage a book of US seller accounts to drive supply health and GMV growth while leading the regional account management team. Act as a player-coach by conducting KPI reviews, coaching team members, and integrating AI tools into daily workflows.
Design, build, and operate Go microservices for seller listings, pricing, and fulfillment within a distributed, event-driven architecture. Own features end-to-end from technical design and deployment to monitoring and production debugging.
Handle complex dispute cases and investigate disputed transactions from cardholders and online payment systems. Collaborate on process improvements and communicate fraud trends to leadership to minimize company losses.
As a Senior Software Engineer, you will work closely with product managers, designers, and other engineers to build features and improvements, while also extending the API and back-end systems. You will solve challenges in various areas such as architecture, security, and performance, and contribute to code reviews and team processes.
This role involves acting as a technical lead to advance recommendation and search algorithms, focusing on improving inventory impression relevance, and developing proprietary AI/ML solutions tailored to the unique marketplace dynamics. Responsibilities also include establishing best practices for model maintenance, owning production deployment, and proactively identifying business problems solvable with data solutions.