Data Scientist & Engineer at nrf

Setup: Hybrid
Location: London, England
Level: mid
Posted: Sep 18, 2026

Description

Norton Rose Fulbright is hiring a Data Scientist & Engineer in its London Legal Innovation, Design & Technology practice group to build governed, reusable data assets and reliable data pipelines in Microsoft Fabric and Databricks. The role combines data engineering and applied data science, including lakehouse modeling, data quality, exploratory analysis, statistical and machine-learning modeling, NLP, embeddings, LLM-assisted techniques, and the publication of analytical products. It requires at least three years of hybrid data-engineering and applied-data-science experience, advanced Python, SQL, and PySpark, and experience with enterprise data governance, testing, APIs, CI/CD, and agentic workflows.

Required Skills

Microsoft Fabric
Databricks
Python
SQL
PySpark
Lakehouse modeling
Data quality
NLP
Embeddings
Git
CI/CD

Additional Information

Required Previous Experiences: At least three years of professional experience in a hybrid data-engineering and applied-data-science role, including delivery of reliable pipelines, reusable data assets, and useful analytical models in Microsoft Fabric and Databricks.
Hands-on experience designing and operating scalable data pipelines, lakehouse data models, Delta and SQL transformations, and data-quality controls in Microsoft Fabric and Databricks.
Practical experience with NLP, information extraction, entity resolution, embeddings, or other techniques relevant to document-heavy and relationship-rich data.
Experience with Git, automated testing, API integration, CI/CD, and version-controlled deployment of data workloads in Microsoft Fabric and Databricks.
Preferred Qualifications: Experience with Azure Data Lake, Azure Data Factory, dbt, Airflow, MLflow, Power BI, Microsoft Purview, or comparable tools.
Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies, or master-data-management techniques.
Experience with MLOps, model monitoring, experimental design, causal inference, forecasting, recommendation systems, or optimisation.
Experience with legal, financial, client, market, or professional-services data, especially licensed external data and restricted information.