# Data Scientist & Engineer

**Company**: Norton Rose Fulbright
**Location**: London
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Data Science
**Industry**: Legal

**Apply**: https://nrf.wd3.myworkdayjobs.com/en-US/External/job/London-United-Kingdom/Data-Scientist---Engineer_R-4379?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_4a6c9689-546

## Description

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.

## Skills

### Required
- Python
- SQL
- PySpark
- Microsoft Fabric
- Databricks
- NLP
- information extraction
- entity resolution
- embeddings
- Git
- automated testing
- API integration
- CI/CD

### Nice to have
- Azure Data Lake
- Azure Data Factory
- dbt
- Airflow
- MLflow
- Power BI
- Microsoft Purview
- search indexes
- vector databases
- graph databases
- knowledge graphs
- ontologies
- master-data-management techniques
- MLOps
- model monitoring
- experimental design
- causal inference
- forecasting
- recommendation systems
- optimisation

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Source: [Apply at nrf.wd3.myworkdayjobs.com](https://nrf.wd3.myworkdayjobs.com/en-US/External/job/London-United-Kingdom/Data-Scientist---Engineer_R-4379?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)