Data Scientist, Fraud

🕒 June 29

🇬🇧 United Kingdom – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 46%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Moniepoint Inc. (Formerly TeamApt Inc.)

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

💳 Fintech

🏦 Banking

Fintech • Banking • Payments

Moniepoint Inc. is Africa's all-in-one financial ecosystem that provides seamless solutions in payments, banking, credit, and business management for over 10 million businesses and individuals. Operating as Nigeria's largest merchant acquirer, Moniepoint powers the majority of Point of Sale (POS) transactions in the country. The company processes $17 billion monthly while ensuring profitable operations. With operations starting in 2019, Moniepoint continues to support businesses through its comprehensive financial services platform, making significant strides in financial inclusion across emerging markets.

📋 Description

• Prototype, evaluate, and help produce machine learning models for fraud detection • Own ongoing model monitoring and retraining cycles • Design and run experiments measuring fraud-intervention impact while balancing customer experience and loss reduction • Size fraud typologies across product lines to inform prioritization and investment decisions • Build and maintain anomaly detection systems to surface novel fraud vectors before they scale • Collaborate with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations • Deliver production-grade ML models and anomaly detection systems • Drive product prioritization and strategic investment decisions through fraud-typology sizing

🎯 Requirements

• 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 experimentation, statistical inference, model evaluation, and feature engineering • Proficiency in Python and SQL • Comfort working across the full model development lifecycle • 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

• People-first culture prioritizing team-member well-being • Learning and development environment • Knowledge sharing and training • Regular internal technical talks • Attractive salary • Pension • Health insurance • Annual bonus • Other benefits

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