Data Scientist, Risk at Flutterwave

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).