Junior Data Scientist in London

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.

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.

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.

What Success Looks Like: The Data Programme and R&D teams have a clearer, evidence-based view of their data before investing in models or product features. Exploratory work produces useful insights, credible hypotheses and well-documented next steps, not only charts or descriptive statistics. The data scientist develops into a reliable contributor to model development, product evaluation and scalable analytical practice.

Essential Skills and Experience:

• An excellent academic record, including a minimum of an MSc or an equivalent Master's degree in data science, statistics, mathematics, computer science, economics, engineering or a closely related quantitative discipline.
• Strong Python and SQL fundamentals, including pandas or comparable data-analysis tools and the ability to work with relational data.
• Sound grasp of descriptive statistics, exploratory analysis, data visualisation, hypothesis testing and the basics of supervised and unsupervised machine learning.
• Evidence of hands-on analytical work through internships, research, coursework, personal projects or an early-career role.
• Ability to reason carefully about data quality, sample selection, missingness, bias, correlation versus causation and uncertainty.
• Familiarity with Git, notebooks, reproducible analysis and basic testing or code-review practices, together with an interest in building reusable data assets rather than one-off analysis.
• Proficiency with AI agents and analytical assistants, with a disciplined approach to validation, data confidentiality and explaining how conclusions were reached.

Desirable Skills and Experience:

• Exposure to Microsoft Fabric or Databricks through coursework, projects, internships or early-career work; familiarity with scikit-learn, Spark, dbt, Power BI or comparable tools is useful.
• NLP, information extraction, embeddings, document analysis, network or graph analysis, or time-series project experience.
• A portfolio, dissertation, research paper, competition entry or other evidence of rigorous analytical curiosity.

Diversity, Equity and Inclusion: To attract the best people, we strive to create a diverse and inclusive environment where everyone can bring their whole selves to work, have a sense of belonging, and realize their full career potential. Our new enabled work model allows our people to have more flexibility in the way they choose to work from both the office and a remote location, while continuing to deliver the highest standards of service.

We are proud to be an equal opportunities employer and encourage applications from individuals who can complement our existing teams. We strive to create an inclusive and accessible recruitment process for all candidates.