Data Scientist & Engineer

About the role

Norton Rose Fulbright is building a new R&D capability to develop data-driven and AI-enabled products for legal services. The Data Scientist & Engineer works within R&D alongside the Data Programme to build foundational data capabilities: trusted, reusable data assets, reliable pipelines, data quality, lakehouse models and analytical products. This is a hands-on hybrid data-engineering and applied-data-science role, with particular responsibility for building data products in Microsoft Fabric and Databricks.

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 data as reusable 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 in Fabric and Databricks workflows.
• Run exploratory data analysis to surface patterns, gaps and anomalies, and develop statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, similarity and extraction.
• Use NLP, embeddings and LLM-assisted techniques responsibly on document-heavy data, and publish curated datasets, features and outputs through Fabric and Databricks products, tables, APIs, search indexes and batch pipelines.
• Maintain reproducible code, tests, model and data documentation, evaluation evidence and clear statements of limitations.
• Use AI agents and coding assistants responsibly, keeping ownership of analytical judgement and verification.

Required profile

• At least three years' professional experience in a hybrid data-engineering and applied-data-science role, delivering reliable pipelines, reusable data assets and analytical models in Microsoft Fabric and Databricks.
• Ability to turn a business question into a well-formed analytical problem and communicate findings, uncertainty and trade-offs clearly.

Required skills

• Advanced Python, SQL and PySpark, including data transformation, analysis and common machine-learning libraries.
• Hands-on experience with scalable pipelines, lakehouse models, Delta and SQL transformations, and data-quality controls in Microsoft Fabric and Databricks.
• Exploratory data analysis, statistics, feature engineering, model selection, validation and error analysis.
• Practical experience with NLP, information extraction, entity resolution and embeddings.
• Data lineage, provenance, metadata, access controls and permissions, and responsible use of sensitive data.
• Git, automated testing, API integration, CI/CD and version-controlled deployment in Microsoft Fabric and Databricks.
• Desirable: Azure Data Lake, Azure Data Factory, dbt, Airflow, MLflow, Power BI, Microsoft Purview, search/vector/graph databases, MLOps, model monitoring, causal inference, forecasting, recommendation systems and legal or financial data.