Data Scientist – Generative AI

Join our Generative AI team at Argus Media to help shape how AI is used across the organisation.

We are looking for a Data Scientist with hands-on experience developing Generative AI applications.

Working alongside other data scientists and engineers, you will help develop customer-facing GenAI solutions and the data capabilities that underpin them.

A key initial focus will be helping make Argus content and data AI-ready, and developing the retrieval, evaluation, and agentic capabilities needed to use that data effectively in AI-powered products.

You will also contribute to the wider development of GenAI capabilities as our technology and use cases evolve.

Responsibilities

As part of a multidisciplinary GenAI team, you will:

• Help develop and improve GenAI capabilities used in Argus products, combining LLMs, retrieval, data, and agentic approaches to solve real customer problems.
• Transform, enrich, and structure Argus content and data so that it can be effectively understood and used by AI systems.
• Develop and improve semantic, vector, and hybrid retrieval capabilities across Argus content and data.
• Develop agentic applications and workflows using LangChain and LangGraph, combining tools, retrieval, and LLM reasoning.
• Help develop APIs, MCP tools, and other reusable capabilities that allow AI agents to interact reliably with data and services.
• Evaluate and improve the quality and reliability of GenAI systems, including retrieval, tools, and agent behaviour.
• Develop high-quality Python components, services, and data pipelines, applying good software engineering practices.
• Work closely with data scientists, engineers, editorial, product, and business teams to develop scalable GenAI solutions.

Requirements

• Strong Python skills, with hands-on experience developing Generative AI applications.
• Practical experience using LangChain and/or LangGraph to build LLM-powered applications or agentic workflows.
• Practical experience with RAG, semantic search, embeddings, vector databases, and modern retrieval techniques.
• Experience integrating LLMs with data, tools, or external services.
• Experience working with and preparing structured or unstructured data for effective use by AI systems.
• An understanding of approaches for evaluating and improving LLM, retrieval, or agentic systems.
• Practical experience with cloud platforms such as Azure, AWS, or Google Cloud and technologies such as Docker.
• Strong problem-solving, communication, and collaboration skills.

Experience with Model Context Protocol (MCP), GraphRAG, or knowledge graph-based retrieval, entity resolution, or LLM evaluation would be an advantage.