Data Scientist (Fraud)

Who we are

Ranked in 2024 by the Financial Times, Moniepoint is Africa’s fastest-growing fintech, trusted by over 10 million business and individual accounts, processing billions of Naira in transactions monthly . Our mission is to enable financial happiness for every African, everywhere .

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:

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Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles .

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Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction .

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Size fraud typologies across our product lines to inform prioritization and investment decisions .

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Build and maintain anomaly detection systems to surface novel fraud vectors before they scale .

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Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations .

Experience & Background:

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A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar).

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3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.

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Hands-on experience building and deploying machine learning models in a production environment.

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Fraud, risk, or financial services experience is a strong plus.

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Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering.

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Comfort working in fast-paced, cross-functional teams with high ownership expectations.

Skills & Competencies:

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Proficiency in Python and SQL; comfort working across the full model development lifecycle .

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An investigative instinct — you enjoy digging into data to find patterns others miss .

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The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action .

What Success Looks Like in This Role:

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Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale .

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Well-designed experiments that successfully balance customer experience against fraud loss reduction .

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Clear sizing of fraud typologies that effectively drives product prioritization and strategic investment decisions .

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Seamless cross-functional alignment where technical model outputs are consistently translated into real-world fraud mitigations .

Why Join Us?

• Culture: We put our people first and prioritize the well-being of every team member . We've built a company where all opinions carry weight and where all voi ces 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, annual bonus, plus other benefits .

Moniepoint is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and candidates.
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Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/job-application-for-head-of-engineering-supply-chain-at-moniepoint/remote-data-scientist-fraud-95114)