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We are looking for a Data Scientist to help shape the future of payments by building data-driven solutions that improve fraud detection, streamline merchant onboarding, enhance customer experiences, and support smarter decision-making across the business.
In this role, you'll work closely with product, engineering, and operations teams to uncover insights from complex datasets and develop predictive models that drive measurable impact.
Build and deploy machine learning models for fraud detection, transaction scoring, and behavioural insights.
Develop statistical models and forecasting tools that improve operational efficiency and reduce risk.
Apply NLP, anomaly detection, clustering, and other advanced techniques to extract value from payments and customer data.
Conduct exploratory data analysis (EDA) to surface trends, anomalies, and optimisation opportunities.
Turn complex datasets into clear, actionable insights through dashboards, visualisations, and reporting.
Partner with stakeholders to define KPIs and measure product performance.
Work alongside data engineers to design scalable data pipelines and ensure high-quality, reliable datasets.
Support integration of structured and unstructured data sources across the wider organisation.
Contribute to the evolution of data lakes, warehouses, and real-time analytics environments.
Collaborate with Product, Risk, Compliance, and Operations teams to align analytics with business goals.
Present insights and recommendations to both technical and non-technical audiences.
Support experimentation initiatives, including A/B tests and data-driven product development.
Ensure all data usage meets internal governance standards and industry regulations (GDPR, PCI-DSS).
Maintain clear documentation of models, methodologies, and data sources for auditability and reproducibility.
Proven experience as a Data Scientist, ideally within payments, fintech, or large-scale analytics environments.
Strong skills in Python, R, SQL, and data science toolkits (e.g., scikit-learn, pandas, TensorFlow).
Hands-on experience with cloud services (AWS, Azure, GCP) and big data frameworks (Spark, Databricks).
Deep understanding of statistical modelling, machine learning techniques, and data visualisation best practices.
Excellent communication skills and the ability to work effectively with cross-functional teams.
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