Fraud Data Scientist (AI)

Location: On-site in Chicago, United States

Base salary: $120,000–$180,000 USD per year plus equity.

Company

hirify.global is a payment service provider building global financial infrastructure with stablecoins, AI-driven fraud prevention, instant settlement, global pay-ins, multi-currency FX, and unified payouts for marketplaces, fintechs, remittance providers, gaming platforms, and ecommerce merchants.

What you will do

• Analyze fraud model and rule performance across approval rates, fraud loss, false positives, model drift, and merchant segments.
• Identify emerging fraud patterns, including fraud rings, account takeover, card testing, friendly fraud, and bonus abuse, and quantify their impact.
• Explore transaction, device, behavioral, identity, and network data; prototype and validate new fraud signals and features.
• Own label-quality analysis across chargebacks, disputes, and manual review, including delayed and noisy ground-truth data.
• Design and evaluate backtests, shadow tests, champion/challenger experiments, and third-party fraud-vendor comparisons.
• Partner with engineering, risk operations, and compliance to tune rules, thresholds, step-up authentication, and merchant-segment strategies, while explaining results to leadership, merchants, banks, and auditors.

Requirements

• 4+ years as a data scientist or analyst working on fraud, risk, or abuse, ideally in payments, fintech, or marketplaces.
• Experience measurably improving production fraud models or rules through new features, better thresholds, lower loss, or higher approval rates.
• Strong SQL and Python skills, with fluency in statistics and machine-learning concepts behind fraud models, including gradient-boosted trees, calibration, and imbalanced data.
• Strong understanding of card-not-present fraud, account takeover, card testing, friendly fraud, chargeback reason codes, and adaptive fraudster behavior.
• Ability to evaluate model performance honestly using messy, imbalanced, and delayed-label data.
• Experience designing and analyzing experiments where false-positive and false-negative costs differ materially.
• Strong feature intuition and the ability to propose signals that could detect observed fraud patterns.
• Ability to explain model behavior and tradeoffs clearly to merchants, risk operations, banks, and auditors.
• Bias toward action and ability to produce clear, evidence-backed recommendations quickly.

Nice to have

• Familiarity with graph or network analysis for detecting linked accounts and fraud rings.
• Comfort working with AI-native tooling as a daily driver, or genuine excitement to start.

Culture & Benefits

• Full-time, on-site work in Chicago.
• Equity is included in addition to the base salary.
• Work spans card, bank, and alternative payment rails across gaming, marketplaces, remittance, and ecommerce.
• Close collaboration with engineering, risk operations, and compliance.