Data Science Team Lead

Linklaters

Linklaters is a global law firm, providing legal services in 20 countries and through 30 offices.

Linklaters is a people business. Being best in class in the eyes of our clients means that our people must be exceptional.

We look not only for brilliant minds, but for people who will thrive in our environment: people who love working collaboratively and demonstrate the innovative, efficient, agile, entrepreneurial, and responsible mind-set we aim to bring to every interaction.

Ours is an environment of outperformance. We achieve this not with targets and incentives, but by fostering a positive, supportive, fair, and open atmosphere.

We respect and value difference but insist on inclusivity. We celebrate all aspects of diversity and challenge any form of bias. This is vital to our ability to work as one team, with a common goal.

Your Role

As Linklaters continues to pioneer a data-driven approach within the legal sector, the significance of AI and Machine Learning in our operations is ever-increasing. We are leveraging these technologies to enhance our legal services, improve client experiences, and maintain our competitive edge. This role is crucial in driving our AI/ML initiatives, as it involves not only technical expertise but also a deep understanding of how these technologies can be applied innovatively in a legal context. Your contributions will be pivotal in shaping the firm's future in AI, impacting both internal processes and client outcomes.

Analyse large structured & unstructured datasets to drive meaningful insights for business functions and leadership teams. Extract and index unstructured data from legal documents using Python packages. Lead the team's delivery across a portfolio of projects spanning predictive modelling, time-series forecasting, experimentation and causal inference, optimisation, and LLM/RAG/GenAI applications — selecting fit-for-purpose techniques (classical ML by default, deep learning and LLMs where genuinely warranted) — with clear business value cases and measured impact. Develop, test and deploy deep learning models to improve document extraction accuracy and document drafting efficiency. Partner with Data Engineering team to design and maintain scalable data pipelines across the firm's Databricks Lakehouse platform. Collaborate with legal, business, Technology teams and clients to understand data requirements, translate legal requirements into technical solutions, and develop and build appropriate data solutions. Horizon scan to keep abreast of technological advancements and evolving tools and techniques. Provide daily tactical guidance to the data science team, establishing coding standards, architectural patterns, and delivery methodologies. Serve as technical authority for AI/ML implementations including agentic workflows, with accountability for codebase quality, scalability, and maintainability. Act as designated cover for senior data leadership during periods of absence, operating at strategic level to maintain operational and strategic continuity. Operate as a revenue-enabler through direct contribution to billable client work, not solely as an internal support function. Perform data engineering functions during resource constraints while maintaining full performance in primary data science responsibilities. Maintain hands-on contribution to critical codebase modules including contract analysis and risk factor functionality. Manage data scientists and provide technical mentorship and code review across the wider data engineering team, contributing to technical capability development and demonstrating leadership reach beyond direct reporting lines.