Description

• Design and deploy intelligent models by applying advanced statistical techniques, traditional machine learning algorithms, deep learning architectures, and modern approaches such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI. Leverage state-of-the-art libraries and frameworks to ensure scalable, production-grade solutions aligned with enterprise deployment standards.
• Lead cross-functional discovery sessions with Data Scientists and Payment Integrity Operations leadership to translate business challenges into actionable analytical objectives. Drive alignment between statistical rigor and emerging AI capabilities to maximise operational impact.
• Deliver robust statistical analyses that frame business scenarios with clarity and precision, integrating classical inference methods with generative and agentic reasoning to inform critical decisions and enhance process outcomes.
• Collaborate with data stewards and engineering teams to curate high-quality datasets for model development. Ensure data integrity and readiness for both traditional predictive modelling and autonomous agent workflows.
• Develop end-to-end modelling pipelines encompassing sampling design, data and selection of appropriate statistical and AI methodologies. Document processes and results with transparency, supporting reproducibility and auditability across both ML and LLM-based systems.
• Communicate insights and model outputs effectively to stakeholders, translating complex statistical and AI-driven findings into actionable business narratives.
• Monitor and refine model performance through continuous evaluation, incorporating feedback loops and adaptive learning mechanisms. Recommend enhancements to algorithms and agentic workflows that unlock new insights and drive measurable improvements.

Requirements:

• Advanced degree in Applied Mathematics, Physics, Computer Science, Statistics, or a related technical field.
• Proficiency in traditional machine learning and statistical modelling, including regression, classification, sampling design.
• Strong coding skills in Python, SQL, and related languages.
• Demonstrated systems-level thinking and a track record of creative problem-solving, including non-standard approaches to complex challenges.
• Proven experience in transitioning projects from proof-of-concept (PoC) to production, with a focus on scalability, reliability, and performance.
• Exposure to LLM technologies

Key Skills

Education

Bachelor's degree