Practice Group / Department:

Legal Innovation, Design & Technology - London

Job Description

Norton Rose Fulbright is a global law firm with more than 3,000 lawyers advising clients across locations in the United States, Europe, Canada, Latin America, Asia, Australia, Africa and the Middle East. We provide a full scope of legal services to the world’s preeminent corporations and financial institutions.

Our vision is to be a world class business, profitable, ambitious, cooperative and considerate, supporting our clients and people through our global business principles of Quality, Unity and Integrity.

With over 7,000 employees worldwide, our culture is the thread that connects us. Our strategy and culture are closely connected – defined by shared ambition, global collaboration and a one-team mindset. We believe pioneering work happens when people are empowered to think beyond boundaries, explore new opportunities and grow through diverse experiences. Alongside the right skills and experience, we are looking for people who are innovative, commercially minded, and motivated by the impact of the work they do – ready to share in our ambition and help shape what comes next.

Because while individuals can do well, together we achieve something extraordinary.

Role Purpose

We are building a new R&D capability focused on developing data-driven and AI-enabled products for legal services and the wider business of law.

The Junior Data Scientist will work within R&D alongside the Data Programme to build the foundational data capabilities that make priority firm data usable for R&D products, AI applications and wider firm use. You will help turn data into evidence, insight and stronger reusable data assets.

This is an early-career role for someone with strong analytical fundamentals who enjoys exploring unfamiliar data, asking what else might be possible, and communicating useful findings clearly.

You will support senior R&D, Data Programme and AI colleagues while taking meaningful ownership of exploratory data analysis, data profiling, analytical prototyping and the measurement of data and AI products.

You will join a small, hands-on multidisciplinary team working collaboratively across discovery, prototyping, engineering, productionisation and continuous improvement.

Key Responsibilities

• Profile and explore internal and external datasets that contribute to Data Programme assets, identifying data-quality issues, missingness, outliers, patterns, relationships and potential sources of bias or leakage.
• Use SQL and Python to prepare and validate analytical datasets in approved Fabric and Databricks workspaces, build reproducible analyses and create clear visualisations and summaries for technical and business audiences.
• Form and test hypotheses; identify where further data, enrichment, analysis or modelling could create additional value for an R&D product or business question.
• Support the development and evaluation of baseline statistical and machine-learning models, including classification, regression, clustering, ranking or time-series approaches where appropriate.
• Assist with text analysis, information extraction, embeddings, similarity analysis and the evaluation of AI-assisted features under the guidance of senior colleagues.
• Help prepare datasets, test cases and measurement frameworks for AI and data products, including human-review samples, error analysis and adoption metrics.
• Work with Data Programme engineers and data stewards to understand source data, document assumptions, define useful metrics and improve the quality and usability of data assets.
• Use Git, notebooks, tests and documentation to make analyses reproducible and ready for peer review rather than leaving work as one-off exploration.
• Use AI agents and assistants responsibly to speed up data exploration, coding and documentation, while checking every result and maintaining independent analytical judgement.
• Engage constructively with lawyers and business colleagues, translating questions into practical analysis and explaining conclusions in accessible language.