Data Scientist, Fraud

Moniepoint Inc. (Formerly TeamApt Inc.)

Data Scientist responsible for developing fraud detection models at Moniepoint. Collaborating with cross-functional teams to evaluate and mitigate fraud threats.

Posted 7/29/2026full-timeRemote • 🇰🇪 KenyaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying machine learning models for fraud detection, with a strong foundation in statistics and data science fundamentals. Collaborates effectively in cross-functional teams to translate model outputs into actionable fraud interventions.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical AnalysisFraud DetectionData Science FundamentalsCross-Functional Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical InferenceModel EvaluationFeature EngineeringExperimentation

Soft Skills

CollaborationOwnershipCommunication

Industry Keywords

Fraud AnalyticsRisk AnalyticsFinancial CrimePaymentsAnomaly Detection

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

You'll receive an attractive salary
pension
health insurance
annual bonus plus other benefits