Principal Data Scientist

Our client is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision-making and outcomes, and advance the rule of law around the world.

They help lawyers win cases, manage their work more efficiently, serve their clients better and grow their practices. They assist corporations in better understanding their markets, monitoring their brands and competition, and in mitigating business risk. They collaborate with universities to educate students, and support nation-building with governments and courts by making laws accessible and strengthening legal infrastructures. They partner with leading global associations and customers to collect evidence against war criminals and provide tools to combat human trafficking.

Responsibilities:

• Design, build, and optimise agentic systems capable of breaking down complex tasks into executable steps. 
• Innovate in context engineering strategies (retrieval-augmented generation, dynamic context construction, memory management) to maximise LLM effectiveness. 
• Select, fine-tune, and adapt large language models (LLMs) to align with agent objectives. 
• Enhance and extend evaluation frameworks for online and offline performance, efficiency, and success rates. 
• Select, benchmark, and host LLMs on Amazon SageMaker or EKS, comparing and optimising models to ensure agentic systems are fast, accurate, and cost-effective. 
• Own and prioritise a backlog of improvements to core intelligence, applying methods such as RLHF and self-learning agents. 
• Collaborate with AI engineering and product teams to deliver production-ready solutions. 
• Mentor and supervise junior data scientists and foster a culture of innovation, collaboration, and continuous improvement. 

Requirements:

• PhD in AI, Computer Science, Mathematics or a related field; or Master’s with equivalent experience. 
• Hands-on experience in LLMs or applied AI research in a commercial environment. 
• Proven expertise in context engineering, LLM fine-tuning, and evaluation of non-deterministic systems.  
• Strong programming skills in Python and proficiency in deep learning frameworks (e.g., PyTorch) 
• Strong problem-solving, analytical, and communication skills. 
• Demonstrated ability to work collaboratively with engineering and product stakeholders in an agile environment.