Machine Learning Lead
Coinflow provides global payment acceptance, instant blockchain settlement, and real-time payouts for online businesses.
Funding history
About Coinflow
Coinflow is a payments company that connects traditional payment methods such as cards and bank transfers with stablecoin settlement on public blockchains. Its APIs and low-code widgets let merchants accept payments, settle funds instantly in USDC, issue real-time payouts, and manage chargeback protection and marketplace split payments across more than 130 countries.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Own the fraud and risk intelligence layer, lead the first dedicated machine learning team, build production fraud models using transaction and behavioral data, manage the model lifecycle, define risk metrics, integrate external fraud partners, establish ML practices, and shape the fraud and risk roadmap.
Requirements
- 5+ years in machine learning, applied data science, or production ML
- Experience building fraud models on the acquiring side of payments
- Experience taking ML projects from proof of concept to production
- Knowledge of authorization fraud, card-not-present fraud, friendly fraud, chargebacks, and merchant risk
- Strong foundation in machine learning, statistics, and feature engineering on high-volume financial data
- End-to-end problem ownership
- Cross-functional collaboration
- Acquirer, ISO, PayFac, or payments infrastructure experience preferred
- MLOps pipeline and monitoring experience preferred
- Cloud compute experience preferred
- Card network rules and dispute workflows preferred
- Real-time fraud scoring experience preferred
- Stablecoin, crypto, or alternative payment rails experience preferred
Responsibilities
- Strengthen fraud detection and risk decisioning capabilities
- Perform feature engineering, model development, and production deployment
- Own experimentation, evaluation, monitoring, and iteration across the model lifecycle
- Define and track fraud and risk metrics
- Explore transaction and behavioral data for fraud signals and attack patterns
- Partner with Engineering, Product, and Operations
- Integrate and orchestrate external fraud and risk partners
- Establish ML and data practices
- Shape the fraud, risk, and ML roadmap
Benefits
- Performance bonus
- Equity grant
- Health and wellness benefits
- 401(k) savings plan
- Flexible time off