Founding Data Scientist
Story Terrace Inc.
- London, England, United Kingdom
- London, England, United Kingdom
Über
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: 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. What You’ll Be Doing
Machine Learning & Modelling
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. Building the Data Science Function
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. Product & Commercial Impact 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. Engineering Collaboration
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. About You
You’ll have:
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. 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: 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
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Sprachkenntnisse
- English
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