About this role:

We're looking for a Data Scientist to sit at the heart of how we fight fraud — building the models, experiments, and detection systems that protect millions of customers and merchants across our platform. This is a high-impact role at the intersection of machine learning, product, and engineering, where your work will directly shape how Moniepoint detects and responds to emerging fraud threats.

You are a data-driven, intellectually curious Data Scientist who is energized by hard problems in fraud and financial crime. You'll prototype and ship ML models, design experiments, and uncover new fraud signals across our ecosystem — partnering closely with engineers, product managers, and analysts to turn your work into production-grade systems.

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.

Experience & Background:

• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar).
• 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.
• Hands-on experience building and deploying machine learning models in a production environment.
• Fraud, risk, or financial services experience is a strong plus.
• Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering.
• Comfort working in fast-paced, cross-functional teams with high ownership expectations.

Skills & Competencies:

• Proficiency in Python and SQL; comfort working across the full model development lifecycle.
• An investigative instinct — you enjoy digging into data to find patterns others miss.
• The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action.

What Success Looks Like in This Role:

• Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale.
• Well-designed experiments that successfully balance customer experience against fraud loss reduction.
• Clear sizing of fraud typologies that effectively drives product prioritization and strategic investment decisions.
• Seamless cross-functional alignment where technical model outputs are consistently translated into real-world fraud mitigations.

• 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.

• Proficiency in Python and SQL; comfort working across the full model development lifecycle.
• An investigative instinct — you enjoy digging into data to find patterns others miss.
• The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action.

• A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar).
• 3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.
• Hands-on experience building and deploying machine learning models in a production environment.
• Fraud, risk, or financial services experience is a strong plus.
• Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering.
• Comfort working in fast-paced, cross-functional teams with high ownership expectations.