Fraud Data Scientist — Shape Global Payments (Equity)

Coinflow; Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

Coinflow in Chicago, IL seeks a Fraud Data Scientist to advance fraud models across card, bank, and alternative rails. You will analyze model performance, spot trends, and propose features, while collaborating with engineers, risk ops, and compliance to turn data into actionable decisions.

You will own experiments, thresholds, and reporting, with a strong emphasis on explainability and practical risk management in a high-growth payments environment.

Qualifications

• 4+ years as a data scientist or analyst working on fraud, risk, or abuse, ideally at a payments company, fintech, or marketplace.
• You've used analysis to measurably improve production fraud models or rules: new features, better thresholds, lower loss, higher approvals.
• Strong SQL and Python. Fluent with the stats and ML concepts behind fraud models (gradient-boosted trees, calibration, imbalanced data).
• Solid grasp of the fraud problem space: card-not-present fraud, account takeover, card testing, friendly fraud, chargeback reason codes, and how fraudsters adapt.
• Comfort with messy, imbalanced, delayed-label data and the judgment to evaluate model performance honestly on it.
• Experience designing and analyzing experiments where a false positive and a false negative cost very different amounts.
• A strong feature intuition: you can look at a fraud pattern and propose the signal that would have caught it.
• Ability to explain model behavior and tradeoffs to merchants, risk ops, banks, and auditors in plain language.
• Familiarity with graph or network analysis for detecting linked accounts and fraud rings is a plus
• Comfort working with AI-native tooling as a daily driver, or genuine excitement to start
• A bias toward action. We'd rather have a clear recommendation this week than a perfect analysis next quarter

Responsibilities

• Model Performance Analysis: Track how our fraud models and rules perform over time and by merchant segment.
• Fraud Trend Analysis: Spot emerging patterns early, from fraud rings and account takeover to card testing, friendly fraud, and bonus abuse.
• Feature Recommendations: Explore transaction, device, behavioral, identity, and network data to find new signals.
• Label Quality & Feedback Loops: Own the ground truth. Make sense of delayed and noisy labels from chargebacks, disputes, and manual review.
• Experimentation & Evaluation: Design and analyze backtests, shadow tests, and champion/challenger experiments.
• Rules & Strategy: Partner with risk operations to tune thresholds, rules, and step-up authentication.
• Reporting & Explainability: Turn analysis into clear reporting for leadership, merchants, banks, and auditors

Skills

SQL

Python

Job description

Coinflow in Chicago, IL seeks a Fraud Data Scientist to advance fraud models across card, bank, and alternative rails. You will analyze model performance, spot trends, and propose features, while collaborating with engineers, risk ops, and compliance to turn data into actionable decisions.

You will own experiments, thresholds, and reporting, with a strong emphasis on explainability and practical risk management in a high-growth payments environment.