Senior Data Scientist

Unchain Data; San Francisco (CA); On-site; USD 153,000 - 205,000; Full time

Job summary

Circle is seeking a Senior Data Scientist on Fraud Analytics to own the prediction loop, turning labels into features and models while moving from vendor scores to Circle's in-house models.

You will partner with Fraud Strategy, Fraud Operations, Risk Engineering, and Compliance to build end-to-end detection for onboarding, login, and money movement, leveraging advanced Python/SQL and rigorous evaluation.

Qualifications

• 5+ years in data science or applied ML in fraud, risk, or financial crime.
• Advanced Python and SQL with experience in XGBoost/LightGBM.
• Experience with sparse, delayed, or biased labels, including cold start, reject inference, and chargeback lag.
• Strong statistics: anomaly detection, graph or network methods, experimentation, and threshold optimization.
• Experience taking models to production and monitoring them.
• Clear communication with technical and non-technical partners.
• Ability to work through ambiguity and limited historical data.

Responsibilities

• Build and evaluate fraud models for onboarding, login, account changes, and money movement. Target risks include account takeover, scams, money mules, and stolen and synthetic identity.
• Evaluate third-party risk signals and design the path from vendor models to Circle models, from shadow mode to production.
• Define the label taxonomy and data requirements with Fraud Operations and Risk Engineering, so every event, decision, and outcome becomes training data. This includes the fraud we miss.
• Design holdout and exploration sampling to measure false negatives and the rate at which we wrongly block good customers.
• Track fraud rate, dispute rate, fraud loss rate, and precision across customer segments. Help set thresholds against an agreed loss budget.
• Work with Fraud Strategy to tune thresholds and rules and respond to emerging attacks.
• Backtest, monitor drift, and retrain models on Circle's fraud platform.
• Help build AI agent workflows for transaction review, fraud-ring analysis, and rule drafting, and evaluate their accuracy against human labels.

Skills

Python

SQL

XGBoost

LightGBM

Fraud domain

Statistics

Production ML

Communication

Ambiguity tolerance

Job description

About Us

Circle (NYSE: CRCL) is one of the world's leading internet financial platform companies, building the foundation of a more open, global economy through digital assets, payment applications, and programmable blockchain infrastructure. Circle's platform includes the world's largest regulated stablecoin network anchored by USDC, Circle Payments Network for global money movement, and Arc, an enterprise-grade blockchain designed to become the Economic OS for the internet. Enterprises, financial institutions, and developers use Circle to power trusted, internet-scale financial innovation.

What You'll Be Part Of

Circle is committed to visibility and stability in everything we do. As we grow as an organization, we're expanding into some of the world's strongest jurisdictions. Speed and efficiency are motivators for our success and our employees live by our company values: High Integrity, Future Forward, Multistakeholder, Mindful, and Driven by Excellence. We have built a flexible work environment where new ideas are encouraged and everyone is a stakeholder.

About The Role

As Circle expands the customers and payment methods it serves, our fraud exposure grows and changes. We rely on conservative limits and third-party models today, and we are building our own.

As a Senior Data Scientist on Fraud Analytics, you will own the prediction side of our fraud loop. You will turn labels into features and models, measure their accuracy, and shift Circle from vendor scores to in-house models. You will work with Fraud Strategy, Fraud Operations, Risk Engineering, and Compliance.

Responsibilities

• Build and evaluate fraud models for onboarding, login, account changes, and money movement. Target risks include account takeover, scams, money mules, and stolen and synthetic identity.
• Evaluate third-party risk signals and design the path from vendor models to Circle models, from shadow mode to production.
• Define the label taxonomy and data requirements with Fraud Operations and Risk Engineering, so every event, decision, and outcome becomes training data. This includes the fraud we miss.
• Design holdout and exploration sampling to measure false negatives and the rate at which we wrongly block good customers.
• Track fraud rate, dispute rate, fraud loss rate, and precision across customer segments. Help set thresholds against an agreed loss budget.
• Work with Fraud Strategy to tune thresholds and rules and respond to emerging attacks.
• Backtest, monitor drift, and retrain models on Circle's fraud platform.
• Help build AI agent workflows for transaction review, fraud-ring analysis, and rule drafting, and evaluate their accuracy against human labels.

Requirements

• 5+ years in data science or applied ML on fraud, risk, or financial crime
• Advanced Python and SQL, and experience with XGBoost, LightGBM, or similar
• Experience building models with sparse, delayed, or biased labels, including cold start, reject inference, and chargeback lag
• Strong statistics: anomaly detection, graph or network methods, experimentation, and threshold optimization
• Experience taking models to production and monitoring them
• Clear communication with technical and non-technical partners
• Comfort working through ambiguity and limited historical data

Preferred Requirements

• Fintech, payments, crypto, or card fraud experience, including chargebacks and disputes
• Experience with device intelligence, behavioral signals, or consortium data
• Familiarity with blockchain analytics, AML, and KYC
• Experience evaluating LLM or agent-based review workflows

Compensation

Base Pay Range: $152,500 - $205,000

Circle considers a wide variety of elements when crafting compensation ranges and total compensation packages. Starting pay is determined by various factors, including but not limited to: relevant experience, skill set, qualifications, and other business and organizational needs. Please note that compensation ranges may differ for candidates in other locations.