Junior Data Scientist

Role Purpose

We are 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 Junior Data Scientist will work within R&D alongside the Data Programme to build the foundational data capabilities that make priority firm data usable for R&D products, AI applications and wider firm use. You will help turn data into evidence, insight and stronger reusable data assets.

This is an early-career role for someone with strong analytical fundamentals who enjoys exploring unfamiliar data, asking what else might be possible, and communicating useful findings clearly.

You will support senior R&D, Data Programme and AI colleagues while taking meaningful ownership of exploratory data analysis, data profiling, analytical prototyping and the measurement of data and AI products.

You will join a small, hands-on multidisciplinary team working collaboratively across discovery, prototyping, engineering, productionisation and continuous improvement.

Key Responsibilities

• Profile and explore internal and external datasets that contribute to Data Programme assets, identifying data-quality issues, missingness, outliers, patterns, relationships and potential sources of bias or leakage.

• Use SQL and Python to prepare and validate analytical datasets in approved Fabric and Databricks workspaces, build reproducible analyses and create clear visualisations and summaries for technical and business audiences.

• Form and test hypotheses; identify where further data, enrichment, analysis or modelling could create additional value for an R&D product or business question.

• Support the development and evaluation of baseline statistical and machine-learning models, including classification, regression, clustering, ranking or time-series approaches where appropriate.

• Assist with text analysis, information extraction, embeddings, similarity analysis and the evaluation of AI-assisted features under the guidance of senior colleagues.

• Help prepare datasets, test cases and measurement frameworks for AI and data products, including human-review samples, error analysis and adoption metrics.

• Work with Data Programme engineers and data stewards to understand source data, document assumptions, define useful metrics and improve the quality and usability of data assets.

• Use Git, notebooks, tests and documentation to make analyses reproducible and ready for peer review rather than leaving work as one-off exploration.

• Use AI agents and assistants responsibly to speed up data exploration, coding and documentation, while checking every result and maintaining independent analytical judgement.

• Engage constructively with lawyers and business colleagues, translating questions into practical analysis and explaining conclusions in accessible language.

Initial Focus

• Support the Data Programme in establishing foundational data capabilities through high-quality EDA, data profiling and rapid analytical investigations in agreed Fabric and Databricks workspaces.

• Build confidence in data quality, metadata, metrics, baselines and user-focused measurement for priority data and AI products.

• Surface informed ideas for what more can be done with available data, then help test those ideas with evidence.

What Success Looks Like

• The Data Programme and R&D teams have a clearer, evidence-based view of their data before investing in models or product features.

• Exploratory work produces useful insights, credible hypotheses and well-documented next steps, not only charts or descriptive statistics.

• The data scientist develops into a reliable contributor to model development, product evaluation and scalable analytical practice.

Essential Skills and Experience

• An excellent academic record, including a minimum of an MSc or an equivalent Master's degree in data science, statistics, mathematics, computer science, economics, engineering or a closely related quantitative discipline.

• Strong Python and SQL fundamentals, including pandas or comparable data-analysis tools and the ability to work with relational data.

• Sound grasp of descriptive statistics, exploratory analysis, data visualisation, hypothesis testing and the basics of supervised and unsupervised machine learning.

• Evidence of hands-on analytical work through internships, research, coursework, personal projects or an early-career role.

• Ability to reason carefully about data quality, sample selection, missingness, bias, correlation versus causation and uncertainty.

• Familiarity with Git, notebooks, reproducible analysis and basic testing or code-review practices, together with an interest in building reusable data assets rather than one-off analysis.

• Proficiency with AI agents and analytical assistants, with a disciplined approach to validation, data confidentiality and explaining how conclusions were reached.

Desirable Skills and Experience

• Exposure to Microsoft Fabric or Databricks through coursework, projects, internships or early-career work; familiarity with scikit-learn, Spark, dbt, Power BI or comparable tools is useful.

• NLP, information extraction, embeddings, document analysis, network or graph analysis, or time-series project experience.

• A portfolio, dissertation, research paper, competition entry or other evidence of rigorous analytical curiosity.