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StockX is seeking a Marketplace Risk Operations Analyst to investigate complex fraud cases and strengthen risk controls across its global marketplace platform for sneakers, apparel, collectibles, electronics, and more.
In this role, you will investigate escalated fraud cases across accounts, transactions, listings, buyers, and sellers, requiring human judgment to uncover patterns. You'll identify coordinated fraud, abuse patterns, and emerging threats by connecting signals across accounts, devices, geographies, and transaction history. You will review AI-generated case recommendations, make informed decisions on edge cases, and provide structured feedback to improve detection models and rules. Your work will surface actionable fraud trends and insights to Risk Operations and Enforcement teams, and you'll execute enforcement actions accurately within SLA requirements.
You'll document investigations with clear, evidence-based findings for escalations, policy reviews, and potential law enforcement requests. You'll identify detection gaps, contribute to fraud pattern reviews and calibration sessions, and help improve fraud monitoring effectiveness. This is a night-shift position based in Bangalore.
StockX is a Detroit-based technology leader founded in 2016, employing 1,000 people across offices and verification centers worldwide. The platform connects buyers and sellers of high-demand consumer goods using dynamic pricing mechanics, featuring hundreds of brands including Jordan Brand, Nike, adidas, Supreme, Louis Vuitton, and collectibles from LEGO, KAWS, and Bearbrick.
REQUIREMENTS:
- 3+ years of experience in Fraud Operations, Trust & Safety, Risk Operations, or similar investigative role
- Hands-on experience with fraud detection tools such as SEON, Sardine, Sift, or similar platforms
- SQL or BI tool knowledge is a plus
- Strong analytical skills with ability to identify patterns across accounts, geographies, and transaction behavior
- Excellent written communication and investigation documentation skills
- Comfortable working in human-in-the-loop AI environments, critically evaluating model recommendations rather than relying on them blindly
