Senior Data Scientist – Fraud

Moniepoint Inc. (Formerly TeamApt Inc.)

Data Scientist developing machine learning models for fraud detection at Moniepoint. Protecting customers and merchants through experimentation and collaboration in a data-driven environment.

Posted 7/7/2026full-timeRemote • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models for fraud detection, with a strong foundation in statistics and data science fundamentals. Proven ability to collaborate cross-functionally and communicate technical insights effectively to drive actionable outcomes.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical AnalysisPython ProficiencySQL ProficiencyCross-Functional Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceStatistical InferenceModel EvaluationFeature Engineering

Soft Skills

CommunicationInvestigative InstinctCollaborationOwnership

Tools & Technologies

Anomaly Detection SystemsFraud Detection Systems

Industry Keywords

Fraud AnalyticsRisk AssessmentFinancial CrimePayments

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Model Development: Prototype, evaluate, and help productionize machine learning models for fraud detection; own their ongoing monitoring and retraining cycles.
Experimentation: Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction.
Risk Assessment: Size fraud typologies across our product lines to inform prioritisation and investment decisions.
System Maintenance: Build and maintain anomaly detection systems to surface novel fraud vectors before they scale.
Cross-Functional Collaboration: 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)
5+ 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
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
Comfort working in fast-paced, cross-functional teams with high ownership expectations

Benefits

Comp & perks

Culture: We put our people first and prioritise the well-being of every team member. We’ve built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human.
Learning: We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks.
Compensation: You’ll receive an attractive salary, pension, health insurance, monthly bonuses, plus other benefits