Opportunity at
MoniepointData Scientist (Fraud)
Moniepoint is recognized as Africa’s fastest-growing fintech by the Financial Times in 2024, serving over 10 million individual and business accounts while processing billions of Naira…
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About This Role
Moniepoint is recognized as Africa’s fastest-growing fintech by the Financial Times in 2024, serving over 10 million individual and business accounts while processing billions of Naira in monthly transactions. The organization seeks a Data Scientist to join its team on-site. In this position, the professional sits within the core fraud prevention group, constructing the detection systems, experiments, and machine learning models necessary to safeguard millions of merchants and consumers across…
Job Description
Moniepoint is recognized as Africa’s fastest-growing fintech by the Financial Times in 2024, serving over 10 million individual and business accounts while processing billions of Naira in monthly transactions. The organization seeks a Data Scientist to join its team on-site.
In this position, the professional sits within the core fraud prevention group, constructing the detection systems, experiments, and machine learning models necessary to safeguard millions of merchants and consumers across the ecosystem. This high-impact role merges machine learning, product management, and engineering to identify and respond to evolving financial crime threats.
This opportunity suits an intellectually curious, data-driven specialist who thrives on solving complex challenges in fraud and risk analytics. Responsibilities include prototyping and shipping machine learning models, designing experiments, and identifying new fraud signals. The practitioner collaborates closely with engineers, product managers, analysts, and fraud operations teams to transition solutions into production-grade systems.
Moniepoint operates with a people-first culture emphasizing staff well-being, mutual respect, and collaborative inclusion. The organization supports continuous learning through training initiatives, knowledge sharing, and routine internal technical discussions.
Responsibilities
- Prototype, evaluate, and help produce machine learning models for fraud detection while managing their ongoing monitoring and retraining cycles
- Design and execute experiments to measure the impact of fraud interventions while balancing customer experience against loss reduction
- Size fraud typologies across product lines to inform prioritization and investment decisions
- Build and maintain anomaly detection systems to uncover novel fraud vectors before they scale
- Partner with fraud operations, engineers, product managers, and data analysts to turn model outputs into practical mitigations
Requirements
- 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
- Proficiency in Python and SQL with comfort working across the full model development lifecycle
- Strong grounding in data science fundamentals including experimentation, statistical inference, model evaluation, and feature engineering
- Ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action
Qualifications
- Degree in a quantitative field such as Statistics, Mathematics, Engineering, or Computer Science
- Prior experience in fraud, risk, or financial services
Core Skills
Benefits
- Attractive salary
- Pension
- Health insurance
- Annual bonus
- Other benefits
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the remote work policy for this role?
The posting specifies an on-site remote status for this position.
What experience is required to apply?
Candidates must have at least three years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime, alongside hands-on experience building and deploying machine learning models in a production environment.
What benefits are provided?
Employees receive an attractive salary, pension, health insurance, annual bonus, and other benefits.
What technical skills are needed?
Applicants need proficiency in Python and SQL, familiarity with the full model development lifecycle, and a strong foundation in statistics, experimentation, statistical inference, model evaluation, and feature engineering.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.
