About Arena Club
Arena Club is building the number one digital marketplace for collectibles. We started by reinventing the hobby with the first-ever digital card show, and we are expanding into new categories so collectors can buy, sell, grade, and showcase everything they care about in one place. Spearheaded by 5x World Series Champion Derek Jeter and serial entrepreneur Brian Lee, our fully digital marketplace is grounded in trust, transparency, and fun, featuring grading and authentication, vaulting, and digital pack openings that let collectors build and display their collections in a personalized online showroom from anywhere in the world.
About the Role
We are seeking a Lead to own our fraud analytics and mitigation strategy. As our founding fraud hire, you will build a proactive, quantitative environment by leading data analysis, developing a robust rules framework, and designing the systemic logic that protects our users.
Over time, you will establish the foundational data architecture for risk operations, paving the way for dedicated data science support as the team expands. This role requires a highly analytical, sharp data problem-solver who can dissect complex transaction anomalies while building the long-term infrastructure to scale and protect our marketplace ecosystem.
What You Will Do
- Establish, statistically test, and scale transactional rules and logic to maximize detection while minimizing friction for legitimate power users
- Develop recurring dashboards, scorecards, and monitoring frameworks to provide clear visibility into fraud performance, portfolio health, and emerging risks
- Conduct deep-dive analyses to diagnose fraud losses, control gaps, false positives, and unexpected performance changes, translating findings into actionable recommendations
- Analyze complex fund flows across credit cards, ACH, digital wallet balances, internal transfers, and high-value payouts to balance platform growth with risk exposure
- Define trusted fraud performance metrics and analytical datasets, including fraud loss rates, approval and decline rates, false-positive rates, customer friction, and operational efficiency
- Quantify and systematically mitigate financial risks, including promotional/referral abuse, card cashing, money laundering, account takeovers, and multi-account abuse
- Partner cross-functionally with Product, Engineering, and Marketing to improve fraud strategies, strengthen operational feedback loops, and support new product launches
- Collaborate closely with Compliance to align fraud detection rules with broader BSA/AML policies, escalation procedures, regulatory reporting, and transaction monitoring processes
Qualifications
- A Bachelor's degree in a relevant field such as Data Analytics, Statistics, Economics, Finance, or Mathematics
- 3+ years of fraud domain experience in financial transaction analysis, anti-money laundering frameworks, and identifying digital payment abuse
- 5+ years of applied analytical experience outside of academia
- Proficient in writing complex SQL queries to pull data, analyze fraud trends, and build automated alerts independently
- Thorough understanding of payment mechanics, transactional fund flows, clearing windows, and risk profiles across ACH, deposits, credit cards, withdrawals, and chargebacks
- Ability to evaluate false-positive rates, analyze data anomalies, and make data-driven decisions to optimize risk controls
- Exceptional judgment and the capacity to operate autonomously, document analytical processes, and solve complex problems in a high-growth startup environment
Preferred Qualifications
- Experience with Metabase
- A solid grasp of e-commerce marketplace vulnerabilities
- Direct experience configuring and optimizing Stripe Radar or Stripe Radar Pro rules
- Broader experience with platform integrity or trust and safety alongside hard financial fraud prevention
The base salary range listed is a guideline. Actual compensation is determined based on skills, experience, and the impact you bring. Total compensation includes base salary, bonus, and equity.