Data Scientist, Risk at Flutterwave

Information is Power! Get new Jobs in your EmailEnter your Email below and click Subscribe: Subscribe Data Scientist, Risk at FlutterwavePosted on Fri 18th Sep, 2026 - hotnigerianjobs.com --- (0 comments) 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 border-less manner. We are recruiting to fill the position below: Job Title: Data Scientist, Risk Location: Lekki, Lagos 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). Application Closing Date Not Specified. How to Apply Interested and qualified candidates should: Click here to apply online Share this job: Independent National Electoral Commission (INEC) 2027 General Elections Nationwide Job Recruitment (5 Positions)Federal Government of Nigeria Job Recruitment (16 Positions)Venite University Massive Academic and Non-academic Job Recruitment (298 Positions)Independent National Electoral Commission (INEC) Massive Ad-hoc Staff Recruitment for 2026 F.C.T Area Council ElectionsFederal Government of Nigeria Nationwide Engineering Mentorship Programme (Graduate Engineers & Skilled Artisans) - 11 PositionsNigeria Police Force (NPF) Massive Job Recruitment (29 Positions)Federal University of Environment and Technology, Koroma / Sakpenwa Massive Graduate and Exp. Academic and Non-academic Job Recruitment (260 Positions)Ahmadu Bello University (ABU) Massive Graduate and Exp. 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• 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. • 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).