Data Scientist (Fraud)

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

All work completed through HarryDesk affordable freelance platform is protected under verified milestone escrow contracts. Once hired, payments are held safely in escrow and released upon client milestone approval.