Data Scientist & Engineer

Norton Rose Fulbright is 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 Data Scientist & Engineer will work within R&D alongside the Data Programme to build foundational data capabilities required for those products: trusted, reusable data assets, reliable pipelines, data quality, lakehouse models and analytical products.

Key Responsibilities

• Partner with the Data Programme, data owners and system teams to assess, access and combine priority internal and external data sources.
• Design, build and operate reliable pipelines in Microsoft Fabric and Databricks to ingest, clean, standardise, enrich, version and publish structured, semi-structured and unstructured data as reusable data assets.
• Create scalable lakehouse data models, schemas, data contracts and reusable features for entities such as clients, organisations, people, matters, sectors, jurisdictions, opportunities and legal topics.
• Implement data-quality checks, lineage, provenance, observability, monitoring and change management within Fabric and Databricks workflows.
• Conduct exploratory data analysis to surface patterns, gaps, anomalies and data-quality issues.
• Develop, compare and validate statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, similarity or extraction problems.
• Use NLP, embeddings and LLM-assisted techniques responsibly for classification, entity resolution, information extraction, clustering and relationship discovery.
• Publish curated datasets, features and analytical outputs through approved Fabric and Databricks data products.
• Maintain reproducible code, tests, model and data documentation, evaluation evidence and clear statements of limitations and appropriate use.

Initial Focus

• Establish foundational, governed and permission-aware data assets in Microsoft Fabric and Databricks.
• Develop reusable pipelines in Microsoft Fabric and Databricks for entity resolution, enrichment, metadata, relationship mapping and data-quality monitoring.
• Run EDA and model development that increases the value of priority data assets.

What Success Looks Like

• The Data Programme and R&D teams can use documented, quality-controlled data assets in Fabric and Databricks.
• Analytical work identifies useful signals and viable opportunities.
• Successful models and features move beyond notebooks into maintainable Fabric and Databricks pipelines, APIs or products.

Essential Skills and Experience

• At least three years' professional experience in a hybrid data-engineering and applied-data-science role.
• Advanced Python, SQL and PySpark.
• Hands-on experience designing and operating scalable data pipelines, lakehouse data models and data-quality controls in Microsoft Fabric and Databricks.
• Strong grounding in exploratory data analysis, statistics, feature engineering, model selection, validation and error analysis.
• Practical experience with NLP, information extraction, entity resolution, embeddings or other techniques.
• Good understanding of data lineage, provenance, metadata, access controls, permissions and responsible use of sensitive data.
• Experience with Git, automated testing, API integration, CI/CD and version-controlled deployment of data workloads.
• Ability to translate business questions into analytical problems and communicate findings clearly.
• Proficiency with AI agents and agentic development workflows.