Staff Product Data Analyst - Trade Surveillance

NinjaTrader's Trade Surveillance team is being built to protect the integrity of our markets and our partners' programs by catching fraud and abuse before it costs anyone money. As a Staff Product Data Analyst, you'll own building and maintaining the in-house detection layer—the models and rules that flag wash trading, collusion, and eval/prop-firm fraud across our trading data—and the partner-facing surveillance reports built on top of them. Engineering builds the data pipeline; you define what runs through it, applying market-structure judgment to turn raw fills into defensible fraud findings that partners and leadership can act on.

In this role you will:

• Own and evolve the detection models and rules that flag market abuse — wash trading, hedging and reverse trading, collusion and coordinated accounts — across our trading data
• Own partner-facing surveillance reporting end to end, starting with standardized end-of-day reports and evolving toward real-time monitoring
• Work directly with eval and prop-firm partners to validate true and false positives, calibrate detection quality, and tune thresholds and rules
• Collaborate with partners on custom reporting and solution feasibility for partner requests
• Translate fraud patterns into precise, tunable detection logic across matching criteria, hold-time thresholds, recurrence windows, and P&L severity scoring
• Maintain data quality and hygiene across the surveillance data that feeds detection and reporting
• Investigate flagged cases end to end and produce clear, evidence-backed findings that partners and internal leadership can act on
• Reduce false positives continuously and surface new fraud patterns as bad actors adapt

What you'll need:

• Deep trade-surveillance or market-risk experience, including hands-on knowledge of market-abuse patterns such as wash trading, hedging, reverse trading, collusion, and copy trading, and how each can produce benign-looking lookalikes
• Working knowledge of futures/derivatives trading mechanics — fills, positions, and P&L — and what abusive trading looks like in practice
• Strong SQL and hands-on experience with a cloud data warehouse (BigQuery and Athena preferred), and comfort investigating large, messy transactional data
• Proven ability to design detection rules or anomaly models and defend them analytically — including precision/recall tradeoffs, thresholds, and validation — with domain judgment leading the analysis
• 10+ years in data analytics or data science, ideally in trading, fraud, risk, fintech, or market surveillance
• Ability to maintain data quality and turn technical findings into clear, concise reporting for partners and executives

Bonus Points for:

• Master's degree in analytics or a related quantitative field
• Prior prop-firm or brokerage experience
• Python or experience with fraud, AML, or surveillance tooling
• Experience building toward real-time or streaming detection