Data Scientist & Engineer

Overview

In this role you will help build an R&D data capability to power AI-enabled legal services. You’ll join a small, hands-on team delivering data assets, pipelines and analytic products in Fabric and Databricks. You’ll collaborate with the Data Programme to turn priority sources into governed data assets for client intelligence and firm-wide decision support. You’ll shape data products from discovery to production, driving impact across the business.

Pay / Benefits

• flexible work location
• family-friendly policies
• health and wellbeing programmes

Responsibilities

• Partner with Data Programme, data owners and system teams to assess, access and combine data sources, ensuring quality and appropriate usage
• Design, build and run pipelines in Microsoft Fabric and Databricks to ingest, clean, standardise, enrich, version and publish data assets
• Create scalable lakehouse models, schemas, data contracts and reusable features for entities like 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 and data-quality issues and identify opportunities for analysis or product use
• Develop, compare and validate ML models for classification, ranking, recommendation, forecasting, anomaly detection, similarity or extraction
• Apply NLP, embeddings and LLM-assisted techniques for classification, entity resolution, information extraction and clustering on document-heavy data
• Publish curated datasets, features and outputs via Fabric/Databricks data products, APIs, search indexes or batch pipelines
• Maintain reproducible code, tests, model and data documentation, evaluation evidence and clear limitations
• Use AI agents and coding assistants responsibly to accelerate work while retaining analytical ownership

Key requirements

• At least three years’ experience in a hybrid data-engineering and applied-data-science role
• Advanced Python, SQL and PySpark with data transformation and ML libraries
• Experience designing and operating scalable data pipelines, lakehouse models, Delta and SQL transformations
• Strong grounding in EDA, statistics, feature engineering, model selection and validation
• Hands-on NLP, information extraction, entity resolution and embeddings with document-heavy data
• Understanding of data lineage, provenance, metadata, access controls and sensitive data handling
• Experience with Git, automated testing, API integration, CI/CD and version-controlled deployment in Fabric/Databricks
• Ability to translate business questions into analytical problems and communicate findings and uncertainties clearly
• Proficiency with AI agents and validated code, data transformations and conclusions

• Collaborative mindset
• Clear communicator
• Structured analytical thinking
• Microsoft Fabric
• Databricks
• Python

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