Lead Data Scientist

Overview About the company Consilio stands as the global leader in eDiscovery, document review, flexible legal talent, and legal advisory & transformation consulting services. With its Consilio Complete suite of capabilities, the company empowers multinational law firms and corporations using innovative software, cost-effective managed services, and deep legal and regulatory industry expertise. Role Overview : The Lead Data Scientist in the Research & Rapid Prototyping team owns the research agenda for a problem area within our eDiscovery platform, translating ambiguous business needs into tractable research questions and driving them through to working prototypes. This is a senior individual contributor role with significant technical influence. You will set the direction and the standard for how we apply Large Language Models and other advanced techniques to enterprise and legal documents, and you will mentor other data scientists on the team as you do it. The role is a natural stepping stone toward people leadership: you will develop and guide others without carrying formal line management responsibility. Responsibilities · Technical Leadership: Own end-to-end delivery of research workstreams — from problem framing and scoping, through experimentation and prototyping, to handoff to engineering and product. Make and defend the technical calls that shape how a problem gets solved. · LLM Specialisation: Set the direction for how we implement, fine-tune, and deploy local Large Language Models for eDiscovery, with a focus on their applicability and efficiency in processing and analysing legal documents such as emails and PDFs. · Evaluation Methodology: Own the evaluation approach the team works to. Define what "good" looks like for legal-specific tasks, build the benchmarks that measure it, and ensure results are trustworthy enough to make decisions on. · Mentorship & Team Development: Mentor junior and senior data scientists through experimental design reviews, code review, and pairing. Raise the bar on research rigour across the team and help others grow into more ambiguous work. · Horizon Scanning: Evaluate emerging models, tools, and techniques, and make clear build/buy/adopt recommendations. Decide what merits investment, and what should be left alone for now. · Rapid Prototyping: Quickly build prototypes that demonstrate how emerging technologies can be applied in practice, exercising the judgement to know when a prototype should move to production or whether to move on. · Statistical Analysis & NLP: Apply statistical analysis and traditional NLP techniques alongside modern methods to extract insights and deepen our understanding of enterprise and legal documents. · Stakeholder Influence: Communicate findings, limitations, and trade-offs clearly to non-technical leadership. Influence the product roadmap based on what is genuinely technically feasible. · Multi-modal & Graph Technologies: (Nice to have) Work…