Fraud Detection Data Scientist - Production ML

Moniepoint Group; Portugal; On-site; EUR 60,000 - 95,000; Full time

Benefits offered by this job

Health insurance

Pension

Annual bonus

Other benefits

Job summary

Moniepoint is seeking a Data Scientist to own fraud-detection models, experiments, and production systems across our platform. You will partner with engineers, product managers, and analysts to turn ML work into real-world mitigations for millions of customers and merchants.

You will prototype, deploy, and monitor models, design experiments to balance customer experience with loss reduction, and size fraud typologies to guide prioritization and investments.

Qualifications

• Degree in Statistics, Mathematics, Engineering or Computer Science.
• 3+ years in data science, decision science, or risk analytics in fraud/finance.
• Hands-on experience building and deploying ML models in production.
• Experience in fraud, risk or financial services is a plus.

Responsibilities

• Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles.
• Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction.
• Size fraud typologies across our product lines to inform prioritization and investment decisions.
• Build and maintain anomaly detection systems to surface novel fraud vectors before they scale.
• Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations.

Skills

Python

SQL

Data analysis

Statistical inference

Communication

Education

Quantitative degree

Job description

Moniepoint is seeking a Data Scientist to own fraud-detection models, experiments, and production systems across our platform. You will partner with engineers, product managers, and analysts to turn ML work into real-world mitigations for millions of customers and merchants.

You will prototype, deploy, and monitor models, design experiments to balance customer experience with loss reduction, and size fraud typologies to guide prioritization and investments.