Staff Product Data Analyst, Fraud & Surveillance

Hidden Jobs; United States; Hybrid; USD 162,000 - 212,000; Full time

Benefits offered by this job

401(k) with company match

PTO 23 days + holidays

Parental leave

Job summary

Hidden Jobs is seeking a Staff Product Data Analyst to own the in-house detection layer for market abuse surveillance in a hybrid Chicago setting. You will design rules, validate detections with partners, and deliver real-time reporting while reducing false positives in dynamic markets.

The ideal candidate has 10+ years in data analytics, strong SQL, and hands-on experience with cloud data warehouses. Preference for fintech, trading, or risk-focused backgrounds and ability to communicate to

Qualifications

• 10+ years in data analytics or data science, preferably in trading, fraud, risk, fintech, or market surveillance.
• Strong SQL and hands-on experience with a cloud data warehouse such as BigQuery or Athena.
• Deep trade-surveillance or market-risk experience with hands-on knowledge of patterns and their benign lookalikes.
• Ability to design detection rules or anomaly models and defend them analytically, including precision/recall tradeoffs, thresholds, and validation.
• Experience turning technical findings into clear, concise reporting for partners and executives.

Responsibilities

• Build and evolve detection logic for market-abuse patterns including wash trading, hedging, reverse trading, collusion, and coordinated accounts.
• Own end-to-end partner-facing surveillance reporting, progressing from standardized end-of-day reports toward real-time monitoring.
• Work directly with partner firms to validate true and false positives, calibrate detection quality, and tune thresholds and rules.
• Translate observed fraud patterns into precise, tunable rules across matching criteria, hold-time thresholds, recurrence windows, and P&L severity scoring.
• Maintain data quality and hygiene across the surveillance datasets that feed detection and reporting.
• Investigate flagged cases end-to-end and produce evidence-backed findings that partners and internal leadership can act on.
• Continuously reduce false positives and surface emerging fraud patterns as bad actors adapt.

Skills

SQL

Trade surveillance

Data analytics

Fraud/Risk experience

Python

Education

Master's degree in analytics

Tools

BigQuery

Athena

Job description

Role overview

A growing trade surveillance function is being assembled to protect market integrity and partner programs by detecting fraud and abuse early. The Staff Product Data Analyst will own the in‑house detection layer — the models and rules that flag wash trading, collusion, and evaluation‑firm fraud — together with the partner‑facing reports built on top of them. The role sits at the intersection of data engineering, market‑structure judgment, and partner collaboration, defining what runs through the pipeline rather than building it.

Responsibilities

• Build and evolve detection logic for market‑abuse patterns including wash trading, hedging, reverse trading, collusion, and coordinated accounts
• Own end‑to‑end partner‑facing surveillance reporting, progressing from standardized end‑of‑day reports toward real‑time monitoring
• Work directly with partner firms to validate true and false positives, calibrate detection quality, and tune thresholds and rules
• Translate observed fraud patterns into precise, tunable rules across matching criteria, hold‑time thresholds, recurrence windows, and P&L severity scoring
• Maintain data quality and hygiene across the surveillance datasets that feed detection and reporting
• Investigate flagged cases end‑to‑end and produce evidence‑backed findings that partners and internal leadership can act on
• Continuously reduce false positives and surface emerging fraud patterns as bad actors adapt

Requirements

• Deep trade‑surveillance or market‑risk experience with hands‑on knowledge of market‑abuse patterns and their benign lookalikes
• Working knowledge of futures and derivatives trading mechanics, including fills, positions, and P&L
• Strong SQL skills and hands‑on experience with a cloud data warehouse such as BigQuery or Athena, with comfort investigating large, messy transactional datasets
• Proven ability to design detection rules or anomaly models and defend them analytically, including precision/recall tradeoffs, thresholds, and validation
• 10+ years in data analytics or data science, ideally in trading, fraud, risk, fintech, or market surveillance
• Ability to turn technical findings into clear, concise reporting for partners and executives

Nice to have

• 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

Benefits and work setup

The salary range is $162,000 to $212,000 USD, plus a 12% annual target bonus split between individual and company/team performance. The package includes a 401(k) with a 3.5% company match, 23 days of accrued PTO plus seven paid holidays, paid parental bonding leave, and health, vision, dental, life, and disability insurance. The role is based in Chicago, IL on a hybrid schedule of in‑office Tuesday through Thursday with remote Mondays and Fridays, plus 20 additional flex remote days annually and six company‑wide office‑optional weeks tied to major holidays. Remote flexibility may be available for exceptional candidates in select U.S. states.