Data Scientist

Must haves: • At least 1- 2 years experience with data • Stakeholder experience • Strong aptitude to learn • Experience working with LLMs programmatically- specifically ChatGPT • Experience building AI assistants/automation tools This is an ideal opportunity for a contractor who enjoys stepping into a well-defined piece of work, moving quickly, and partnering with a mature, collaborative team. You’ll work on meaningful problems with modern tooling, including LLMs, RAG, GenAI, predictive modelling, and agentic workflows, in an environment that values autonomy, technical depth, and low-friction delivery . What you’ll be doing in this 6-month engagemen • • t: 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 standar • • ds.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 impa • • ct.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 outcom • • es.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 workflo • • ws.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 syste • • ms.Communicate insights and model outputs effectively to stakeholders, translating complex statistical and AI-driven findings into actionable business narrativ • • es.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 improvemen ts.