Data Scientist & Engineer for AI-Driven Data Products

Norton Rose Fulbright; City of Westminster; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Norton Rose Fulbright is building a new R&D data capability to develop data-driven and AI-enabled products for legal services. You will work on data pipelines, lakehouse models and analytical products using Microsoft Fabric and Databricks.

The role involves applying NLP and ML methods to document-heavy data, ensuring data quality and governance, and collaborating across discovery, prototyping, engineering and productionisation teams in a hybrid UK setting.

Qualifications

• At least three years' professional experience in a hybrid data-engineering and applied-data-science role.
• Strong grounding in exploratory data analysis, statistics, feature engineering, model selection and validation.
• Experience with NLP, information extraction, embeddings or related techniques for document-heavy data.

Responsibilities

• Partner with Data Programme to assess, access and combine data sources.
• Design, build and operate pipelines in Microsoft Fabric and Databricks.
• Create lakehouse data models, schemas and reusable features.
• Implement data-quality checks, lineage and observability.
• Develop and validate ML models for classification, ranking, forecasting, etc.
• Publish datasets and features through approved Fabric/Databricks products.
• Maintain reproducible code, tests and documentation.
• Translate business questions into analytical problems and communicate findings.

Skills

Python

SQL

PySpark

NLP

LLM

Data engineering

AI/ML

Git

CI/CD

API integration

Tools

Microsoft Fabric

Databricks

Azure Data Lake

Azure Data Factory

dbt

Airflow

MLflow

Power BI

Purview

Job description

Norton Rose Fulbright is building a new R&D data capability to develop data-driven and AI-enabled products for legal services. You will work on data pipelines, lakehouse models and analytical products using Microsoft Fabric and Databricks.

The role involves applying NLP and ML methods to document-heavy data, ensuring data quality and governance, and collaborating across discovery, prototyping, engineering and productionisation teams in a hybrid UK setting.