Founding Data Scientist

Legal 500 was founded by John Pritchard in 1987 as the original clients’ guide to law firms, the first of its kind. It is now a data‑driven, AI‑optimised research platform which benchmarks, informs and connects providers and users of legal services in over 100 countries worldwide. Our research and data are trusted and relied upon by corporate clients globally as an essential part of the process, both of instructing law firms with new mandates, and when reviewing existing mandates or panels. We exist to empower both buyers and sellers in the international legal marketplace to make better decisions and have improved outcomes for their organisations. This is achieved by leveraging a trusted, comprehensive research process with a unique, vast, proprietary and constantly updated set of client‑supplied data, unrivalled in the market. On the supply side of the legal market, every year Legal 500’s team of over 150 researchers, technologists, data analysts, journalists and content specialists collate and review 60,000+ data‑submissions from law firms and conduct interviews with thousands of leading law‑firm partners. On the demand side, Legal 500 analyses confidential data from 300,000+ commercial law‑firm clients to benchmark law firms and lawyers by practice area; industry; jurisdiction; as well as by proprietary client satisfaction metrics, NPS®, and other qualitative and quantitative criteria. Legal 500 is the only source of this depth of global research and data on law firms, lawyers and their clients.

We are looking for a hands‑on Founding Data Scientist to help shape the future of data science at The Legal 500. You will be the first dedicated Data Scientist in our organisation, helping establish and shape data science as a discipline.

This is an opportunity to be the founding Data Scientist, working directly with the Head of Data to build models, uncover insights, and develop machine‑learning solutions that will influence product development, editorial intelligence, and commercial decision‑making.

You’ll work on complex, real‑world datasets from unstructured qualitative submissions to global market metrics and use advanced analytics to unlock new opportunities for automation, prediction, and insight generation.

Your mission will be to:

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• Define how data science is practised at Legal 500
  
• Build robust, production‑ready ML models
  
• Translate unstructured and structured data into meaningful insight
  
• Work closely with Data Engineering and Product to embed data science into products and internal tools
  

This role is ideal for a mid‑senior Data Scientist who has already delivered real‑world ML models end‑to‑end and is now ready to take on more ownership, influence, and strategic involvement.

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• Design, build, and deploy machine learning and deep learning models.
  
• Transform complex unstructured data (e.g., text‑heavy submissions) into structured, model‑ready datasets.
  
• Develop predictive, descriptive, and NLP‑driven models to support internal teams and product innovation.
  
• Visualise and communicate model outputs to stakeholders in clear, compelling ways.
  

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• Establish best practices, frameworks, and model evaluation standards.
  
• Help shape how data science integrates with Data Engineering and Product.
  
• Influence roadmap decisions by identifying viable data science opportunities.
  

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• Translate strategic business questions into analytical frameworks.
  
• Identify opportunities for data science to enhance rankings, client products, and internal workflows.
  
• Test and validate models for robustness, accuracy, and ethical use.
  

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• Work closely with Data Engineers to embed data cleaning and transformation into pipelines.
  
• Help operationalise models in production environments (preferably using Azure endpoints).
  
• Contribute to evolving MLOps practices, version control, and testing frameworks.
  

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• Proven experience building and deploying machine‑learning models end‑to‑end.
  
• Strong proficiency in Python, including scikit‑learn, TensorFlow/PyTorch, and modern ML tooling.
  
• Experience working with unstructured data, NLP, embeddings, LLMs, or vector search.
  
• Solid SQL skills and experience with Snowflake/dbt (or similar).
  
• Understanding of best practices for model quality, ethics, validation, and monitoring.
  
• Ability to explain complex concepts to non‑technical audiences.
  

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• Thrives in a hands‑on, high‑ownership environment.
  
• Enjoys solving ambiguous data problems with practical, scalable solutions.
  
• Communicates clearly and collaborates confidently with cross‑functional teams.
  
• Is curious, adaptable, and keen to stay up to date with modern data science approaches.
  

Nice to Have:

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• Experience deploying ML models or APIs in Azure.
  
• Familiarity with MLOps tools and CI/CD workflows.
  
• Exposure to B2B, publishing, SaaS, or research‑driven organisations.
  
• Experience working with unstructured data.