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# Product Data Analyst, Fraud & Surveillance

- **Company:** Ninja Transfers, LLC
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $162,000.0 - $212,000.0
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, BigQuery, Python (Programming Language), Standard Sql, Transaction Data, Data Analytics, Data Pipelines
- **Published:** September 27, 2026
- **Apply:** https://www.dice.com/job-detail/9b059f4b-41e4-46d9-a8e0-cae1ca9768e3

## About the Role

* 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

## Description

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


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