Fraud Data Scientist
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 29, 2026. Coinflow Labs scores A on the Alion truth index.
About Coinflow
Coinflow is the next-generation payment service provider revolutionizing global financial infrastructure with stablecoins, AI-driven fraud prevention, and instant settlement. Coinflow enables businesses to grow faster with instant settlement, fraud & chargeback indemnity, global pay-ins, multi-currency FX, and unified payouts. Founded in 2023, the company serves marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants worldwide.
Since our seed round in 2024, we’ve achieved 23x revenue growth and scaled to multi-billion-dollar annual transaction volume. In response to this growth, Coinflow announced a $25M Series A in October 2025-led by Pantera Capital, CMT Digital, Coinbase Ventures, Jump Crypto, and Reciprocal Ventures-accelerating our mission to power the world’s fastest-moving businesses with innovative, reliable global payments.
Coinflow is proudly headquartered in Chicago, IL. Learn more at coinflow.cash.
About The Role
We're hiring a Fraud Data Scientist to be the analytical brain behind our fraud models. You'll dig into how our models and rules are performing, spot where fraud is shifting, and tell the team exactly which features, signals, and strategy changes will move the numbers.
You'll work on fraud across card, bank, and alternative payment rails, for merchants in gaming, marketplaces, remittance, and ecommerce, where fraud patterns shift weekly. You'll partner closely with the engineers who build and deploy our models, plus risk operations and compliance, turning data into decisions they can act on. You'll be hands-on with AI-native tooling as a force multiplier for analysis, feature exploration, and experimentation.
What You'll Own
Model Performance Analysis: Track how our fraud models and rules perform over time and by merchant segment. Dig into approval rates, fraud loss, false positives, and drift, and explain what changed and why.
Fraud Trend Analysis: Spot emerging patterns early, from fraud rings and account takeover to card testing, friendly fraud, and bonus abuse. Size the impact and get ahead of it before it shows up in chargebacks.
Feature Recommendations: Explore transaction, device, behavioral, identity, and network data to find new signals. Prototype and validate features, then hand engineering a clear, evidence-backed case for what to add to the models.
Label Quality & Feedback Loops: Own the ground truth. Make sense of delayed and noisy labels from chargebacks, disputes, and manual review, and flag where bad labels are misleading the models.
Experimentation & Evaluation: Design and analyze backtests, shadow tests, and champion/challenger experiments. Evaluate third-party fraud vendors against our own models on our data, and give a clear answer on what to trust.
Rules & Strategy: Partner with risk operations to tune thresholds, rules, and step-up authentication. Recommend decision strategies per merchant segment and quantify the tradeoff between loss and approvals.
Reporting & Explainability: Turn analysis into clear reporting for leadership, merchants, banks, and auditors: what drives model decisions, how performance is trending, and what we're doing about it.
What We're Looking For
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) even if you're not the one deploying them
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
The base salary range for this role is $120,000 - $180,000 USD + equity. The actual base salary offered depends on a variety of factors, including but not limited to experience, education, skills, qualifications, and business needs.
Join the team rewriting how money moves worldwide-and become a driving force in the $194 trillion cross-border payments market.