Junior Data Scientist

Overview

Join our R&D team to build foundational data capabilities for AI-enabled legal services products. You will turn data into evidence, insight and reusable assets, supporting senior colleagues in exploratory analysis and model evaluation. This early-career role combines discovery, prototyping and productionisation in a small, multidisciplinary team. You’ll work closely with lawyers and business colleagues to translate questions into practical analyses and measurable outcomes.

Pay / Benefits

• flexible work model (office and remote)
• family-friendly policies
• health and wellbeing programmes
• inclusive recruitment process
• career development opportunities
• equal opportunities employer

Responsibilities

• Profile and explore internal and external datasets to identify quality issues, patterns and potential bias
• Use SQL and Python in Fabric and Databricks to prepare datasets, run analyses and create visualisations for technical and business audiences
• Form and test hypotheses; identify data or modelling opportunities to add value to R&D products
• Support development and evaluation of baseline statistical and ML models (classification, regression, clustering, ranking, time-series)
• Assist with text analysis, embeddings, information extraction and AI-assisted feature evaluation
• Prepare datasets, test cases and measurement frameworks for AI/data products, including human-review samples and adoption metrics
• Collaborate with Data Programme engineers and data stewards to document assumptions and improve data usability
• Maintain reproducibility with Git, notebooks, tests and documentation; ensure peer review readiness
• Use AI agents responsibly to accelerate exploration while maintaining independent analytical judgement
• Engage with lawyers and business colleagues, translating questions into practical analysis and communicating conclusions

Key requirements

• MSc or equivalent Master’s degree in data science, statistics, mathematics, computer science, economics, engineering or related quantitative discipline
• Strong Python and SQL fundamentals including pandas and relational data handling
• Solid grounding in descriptive statistics, exploratory analysis, data visualization, hypothesis testing and basics of supervised/unsupervised ML
• Hands-on analytical experience from internships, research, coursework or early-career roles
• Ability to reason about data quality, sampling, missingness, bias and uncertainty
• Familiarity with Git, notebooks, reproducible analysis and basic testing/code-review practices
• Proficiency with AI agents/analytical assistants and data confidentiality awareness

• strong communication and ability to explain complex findings to non-technical audiences
• collaboration and teamwork in a multidisciplinary setting
• curiosity and proactive problem solving
• Python (including pandas)
• SQL
• Descriptive statistics and hypothesis testing

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