What we're looking for
We 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.
Responsibilities
- Lead 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.
Qualifications
- 5+ 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).
