As a Data Scientist, you will be responsible for developing advanced analytics, predictive models, and data-driven solutions that enhance payment processing, fraud detection, merchant onboarding, and customer experience. You will work closely with product, engineering, and operations teams to unlock insights from complex datasets and support strategic decision‑making.
Advanced Analytics & Modelling
* Design and implement machine learning models for fraud detection, transaction scoring, and behavioural analysis.
* Develop statistical models and forecasting tools to support operational efficiency and risk mitigation.
* Apply NLP, anomaly detection, and clustering techniques to uncover patterns in payment and customer data.
Data Exploration & Insight Generation
* Perform exploratory data analysis (EDA) to identify trends, outliers, and opportunities for optimisation.
* Translate complex data into actionable insights through dashboards, reports, and visualisations.
* Collaborate with business stakeholders to define KPIs and measure product performance.
Data Engineering Collaboration
* Work with data engineers to build scalable data pipelines and ensure data quality, consistency, and availability.
* Support integration of structured and unstructured data sources across Our Client services.
* Contribute to the design of data lakes, warehouses, and real‑time analytics platforms.
Stakeholder Engagement
* Partner with Product Owners, Risk, Compliance, and Operations to align analytics with business goals.
* Present findings and recommendations to technical and non‑technical audiences.
* Support experimentation, A/B testing, and data‑driven product development.
Governance & Compliance
* Ensure data usage complies with internal governance policies and external regulations (e.g., GDPR, PCI‑DSS).
* Maintain documentation of models, methodologies, and data sources for audit and reproducibility.
Qualifications
* Proven experience as a Data Scientist in payments, fintech, or enterprise analytics environments.
* Strong proficiency in Python, R, SQL, and data science libraries (e.g., scikit-learn, pandas, TensorFlow).
* Experience with cloud platforms (AWS, Azure, GCP) and big data tools (e.g., Spark, Databricks).
* Solid understanding of statistical modelling, machine learning, and data visualisation techniques.
* Excellent problem‑solving, communication, and stakeholder engagement skills.
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