Fraud Detection Data Scientist — Production ML

Moniepoint; United States; On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Pension

Health insurance

Annual bonus

Job summary

Moniepoint in the United States is seeking a Data Scientist to join our fraud prevention team, building models, experiments, and detection systems that protect millions of customers and merchants across our platform.

You will prototype and deploy ML models, design experiments, and uncover new fraud signals, collaborating with engineers, product managers, and analysts to translate insights into production-grade mitigations.

Qualifications

• 3+ years in data science, decision science, or risk analytics within fraud, payments, or financial crime.
• Hands-on experience building and deploying machine learning models in production.
• Solid grounding in experimentation, statistical inference, model evaluation, and feature engineering.
• Comfort working in fast-paced, cross-functional teams with high ownership expectations.

Responsibilities

• Prototype, evaluate, and help produce ML models for fraud detection; own monitoring and retraining cycles.
• Design and run experiments to measure impact of fraud interventions balancing customer experience and loss reduction.
• Size fraud typologies across product lines to inform prioritization and investment decisions.
• Build and maintain anomaly detection systems to surface novel fraud vectors.
• Work with fraud operations, engineers, product managers, and data analysts to translate model outputs into mitigations.

Skills

Python

SQL

Statistics

Fraud detection

Data analysis

Model deployment

Education

Degree in quantitative field

Job description

Moniepoint in the United States is seeking a Data Scientist to join our fraud prevention team, building models, experiments, and detection systems that protect millions of customers and merchants across our platform.

You will prototype and deploy ML models, design experiments, and uncover new fraud signals, collaborating with engineers, product managers, and analysts to translate insights into production-grade mitigations.