Lekki, Lagos · hace 5 meses
Desarrollar, desplegar y monitorear modelos de aprendizaje automático para detectar fraude, evaluar riesgos y proteger la infraestructura de pagos de Flutterwave. Traducir los resultados de los modelos en estrategias de riesgo junto con los equipos de cumplimiento y operaciones.
Flutterwave was founded on the principle that every African must be able to participate and thrive in the global economy. To achieve this objective, we have built a trusted payment infrastructure that allows consumers and businesses (African and International) make and receive payments in a convenient borderless manner. The Role Flutterwave is seeking a highly analytical and technically driven Data Scientist to join our dynamic Risk team. As a fintech company operating in a rapidly evolving landscape, detecting anomalies and mitigating risk at scale is paramount to our success. In this role, you will contribute to the transition of our risk capabilities from reactive analytics to proactive predictive modeling. You will be responsible for designing, training, and deploying robust machine learning models to detect fraud, assess risk, and protect our infrastructure. The ideal candidate possesses a deep understanding of ML algorithms, strong engineering fundamentals, and the ability to translate complex data into scalable, automated risk solutions. Responsibilities: • Model Development: Design, develop, and optimize data-driven algorithms and machine learning models specifically focused on fraud detection, transaction monitoring, and risk mitigation. • Feature Engineering: Collect, clean, and analyze massive transactional datasets from multiple sources to identify predictive features and emerging fraud trends. • Deployment & Monitoring: Partner directly with the Engineering and MLOps teams to deploy models into production environments. Monitor model performance, track data drift, and retrain models to ensure ongoing accuracy. • Risk Strategy Optimization: Translate complex model outputs into actionable business rules and strategies. Work with the compliance and operations teams to balance fraud prevention with user friction. • Data Pipeline Validation: Collaborate with cross-functional teams to validate data accuracy, integrity, and consistency across all machine learning and risk pipelines. • Documentation & Culture: Maintain accurate, up-to-date documentation of model architectures, training datasets, and algorithmic decisions to support internal and external audits and regulatory requirements. Required Competency and Skillset: • Education: Bachelor's or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field. • Experience: 3–5 years of proven experience as a Data Scientist building and deploying machine learning models in a production environment. • Industry Knowledge: Experience working in the Fintech or payments industry is required. • Bonus/Preferred: Direct experience building fraud detection, credit risk, or anti-money laundering (AML) models is a plus. • Technical Stack: Strong programming skills in Python and SQL. Deep proficiency with ML libraries (e.g., Scikit-Learn, XGBoost, LightGBM, TensorFlow, or PyTorch). • Data Tools: Experience working with cloud data warehouses (e.g., Redshift, Snowflake, BigQuery). • Soft Skills: Exceptional problem-solving skills and the ability to communicate complex, algorithmic concepts clearly to non-technical stakeholders (Legal, Finance, Operations). Click here for the Recruitment Privacy Notice and Flutterwave Privacy Policy
Publicada el 1 jun 2026
