Data Scientist (Fraud)

<p>&nbsp;</p> <p><strong>Who we are</strong></p> <p><span data-path-to-node="12,1"><span class="citation-119">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</span></span><span data-path-to-node="12,3">.&nbsp;</span><span data-path-to-node="12,5"><span class="citation-118">Our mission is to enable financial happiness for every African, everywhere</span></span><span data-path-to-node="12,7">.</span></p> <p data-path-to-node="6"><strong data-path-to-node="6" data-index-in-node="0">About this role:</strong></p> <p id="p-rc_0cd24779a7324afc-115" data-path-to-node="7"><span data-path-to-node="7,0">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</span><span data-path-to-node="7,2">. 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</span><span data-path-to-node="7,4">.</span></p> <p id="p-rc_0cd24779a7324afc-116" data-path-to-node="8"><span data-path-to-node="8,0">You are a data-driven, intellectually curious Data Scientist who is energized by hard problems in fraud and financial crime</span><span data-path-to-node="8,2">. 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</span><span data-path-to-node="8,4">.</span></p> <p data-path-to-node="9"><strong data-path-to-node="9" data-index-in-node="0">Responsibilities:</strong></p> <ul data-path-to-node="10"> <li> <p id="p-rc_0cd24779a7324afc-117" data-path-to-node="10,0,0"><span data-path-to-node="10,0,0,0">Prototype, evaluate, and help produce machine learning models for fraud detection; own their ongoing monitoring and retraining cycles</span><span data-path-to-node="10,0,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-118" data-path-to-node="10,1,0"><span data-path-to-node="10,1,0,0">Design and run experiments to measure the impact of fraud interventions, balancing customer experience against loss reduction</span><span data-path-to-node="10,1,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-119" data-path-to-node="10,2,0"><span data-path-to-node="10,2,0,0">Size fraud typologies across our product lines to inform prioritization and investment decisions</span><span data-path-to-node="10,2,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-120" data-path-to-node="10,3,0"><span data-path-to-node="10,3,0,0">Build and maintain anomaly detection systems to surface novel fraud vectors before they scale</span><span data-path-to-node="10,3,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-121" data-path-to-node="10,4,0"><span data-path-to-node="10,4,0,0">Work closely with fraud operations, engineers, product managers, and data analysts to translate model outputs into real-world mitigations</span><span data-path-to-node="10,4,0,2">.</span></p> </li> </ul> <p data-path-to-node="11"><strong data-path-to-node="11" data-index-in-node="0">Experience &amp; Background:</strong></p> <ul data-path-to-node="12"> <li> <p>A strong foundation in statistics with a degree in a quantitative field (Statistics, Mathematics, Engineering, Computer Science, or similar).</p> </li> <li> <p>3+ years of experience in data science, decision science, or risk analytics within fraud, payments, or financial crime.</p> </li> <li> <p>Hands-on experience building and deploying machine learning models in a production environment.</p> </li> <li> <p>Fraud, risk, or financial services experience is a strong plus.</p> </li> <li> <p>Solid grounding in data science fundamentals: experimentation, statistical inference, model evaluation, and feature engineering.</p> </li> <li> <p>Comfort working in fast-paced, cross-functional teams with high ownership expectations.</p> </li> </ul> <p data-path-to-node="13"><strong data-path-to-node="13" data-index-in-node="0">Skills &amp; Competencies:</strong></p> <ul data-path-to-node="14"> <li> <p id="p-rc_0cd24779a7324afc-126" data-path-to-node="14,0,0"><span data-path-to-node="14,0,0,0">Proficiency in Python and SQL; comfort working across the full model development lifecycle</span><span data-path-to-node="14,0,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-127" data-path-to-node="14,1,0"><span data-path-to-node="14,1,0,0">An investigative instinct — you enjoy digging into data to find patterns others miss</span><span data-path-to-node="14,1,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-128" data-path-to-node="14,2,0"><span data-path-to-node="14,2,0,0">The ability to communicate technical findings clearly to non-technical stakeholders and translate insights into action</span><span data-path-to-node="14,2,0,2">.</span></p> </li> </ul> <p data-path-to-node="15"><strong data-path-to-node="15" data-index-in-node="0">What Success Looks Like in This Role:</strong></p> <ul data-path-to-node="16"> <li> <p id="p-rc_0cd24779a7324afc-129" data-path-to-node="16,0,0"><span data-path-to-node="16,0,0,0">Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale</span><span data-path-to-node="16,0,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-130" data-path-to-node="16,1,0"><span data-path-to-node="16,1,0,0">Well-designed experiments that successfully balance customer experience against fraud loss reduction</span><span data-path-to-node="16,1,0,2">.</span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-131" data-path-to-node="16,2,0"><span data-path-to-node="16,2,0,0">Clear sizing of fraud typologies that effectively drives product prioritization and strategic investment decisions</span><span data-path-to-node="16,2,0,2"><span class="citation-39 citation-end-39">.</span></span></p> </li> <li> <p id="p-rc_0cd24779a7324afc-132" data-path-to-node="16,3,0"><span data-path-to-node="16,3,0,0"><span class="citation-38 citation-end-38">Seamless cross-functional alignment where technical model outputs are consistently translated into real-world fraud mitigations</span></span><span data-path-to-node="16,3,0,2"><span class="citation-37 citation-end-37">.</span></span></p> </li> </ul> <p id="p-rc_0cd24779a7324afc-138" data-path-to-node="17"><strong data-path-to-node="17" data-index-in-node="0"><span class="citation-36 citation-end-36">Why Join Us?</span></strong></p> <ul> <li data-path-to-node="17"><span data-path-to-node="18,0"><strong data-path-to-node="18,0" data-index-in-node="2"><span class="citation-35">Culture:</span></strong><span class="citation-35 citation-end-35"> We put our people first and prioritize the well-being of every team member</span></span><span data-path-to-node="18,2"><span class="citation-34 citation-end-34">. We've built a company where all opinions carry weight and where all voi</span>ces are heard</span><span data-path-to-node="18,4">. We value and respect each other and always look out for one another</span><span data-path-to-node="18,6">. Above all, we are human</span><span data-path-to-node="18,8">.</span></li> <li data-path-to-node="17"><span data-path-to-node="19,0"><strong data-path-to-node="19,0" data-index-in-node="2">Learning:</strong> We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks</span><span data-path-to-node="19,2">.</span></li> <li data-path-to-node="17"><span data-path-to-node="20,0"><strong data-path-to-node="20,0" data-index-in-node="2">Compensation:</strong> You'll receive an attractive salary, pension, health insurance, annual bonus, plus other benefits</spa