Lead Data Scientist

What we’re looking forWe are looking for a highly capable and curious Data Scientist to help build the data and ML capabilities powering our B2B payments platform, which processes over US$2B annually across multiple currencies. You’ll work closely with engineering and product teams to solve complex problems, drive data-driven decisions, and own models end-to-end — from exploration to deployment. If you're excited by impactful work in a fast-moving fintech environment, we’d love to meet you.ResponsibilitiesLead and mentor a cross-functional data team (data science, data engineering, analytics) to ensure high-quality, timely execution.Own end-to-end delivery of machine learning models — from scoping and development to deployment, monitoring, and iteration.Analyse large, complex datasets to identify insights, diagnose issues, and validate opportunities that drive measurable business impact.Oversee development of reliable data pipelines, models, and dashboards to support operational, product, and compliance needs.Design and run A/B tests; define and monitor key metrics; forecast trends and communicate insights to leadership.Collaborate closely with Product, Engineering, Operations, and Compliance to translate business problems into actionable data and ML solutions — Fraud & Anomaly Detection, Dynamic Routing.Identify and prioritise high-value opportunities where ML, analytics, or improved data infrastructure can enhance platform performance and growth.Qualifications5+ years of hands-on experience in data science or machine learning.Strong proficiency in Python and SQL, plus ML libraries (scikit-learn, PyTorch/TensorFlow).Proven track record of deploying ML models into production environments.Experience building reliable pipelines, working with real-time / batch data, and monitoring model performance.Strong communication skills and the ability to work across technical and non-technical teams.Experience working in fintech, payments, or high-growth tech environments (preferred).Familiarity with APIs, microservices, and MLOps tooling (Airflow, CI/CD, monitoring).