Job Description

The Data Scientist will be responsible for driving data-informed decision-making and building advanced data models to support strategic business objectives. This role requires strong analytical and technical skills, deep experience in data science methodologies, and the ability to transform complex data into actionable insights. The Data Scientist will collaborate closely with AI Engineering, Legal AI Research, and Product teams to design and deploy scalable solutions leveraging modern machine learning, data engineering, and analytics practices.

Key Responsibilities

• Design, build, and deploy advanced data models, algorithms, and analytical frameworks to support AI and product initiatives.
• Collect, process, and analyse large structured and unstructured datasets to generate actionable insights.
• Apply statistical modelling, machine learning, and data mining techniques to solve complex business problems.
• Collaborate with cross-functional teams to identify opportunities for leveraging data to drive business solutions.
• Build scalable data pipelines and integrate analytical models into production environments.
• Develop and maintain dashboards, visualisations, and reporting systems to track model performance and business metrics.
• Contribute to experimentation strategies and design A/B testing frameworks for model and product evaluation.
• Stay current with advancements in data science, ML, and AI technologies, and proactively apply new methods and tools.
• Ensure data quality, governance, and compliance standards are upheld in all modelling and analysis work.
• Provide technical guidance and mentorship to junior data scientists and analysts.

Required Skills and Qualifications

• Master's or PhD in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience in data science or applied machine learning.
• Proficiency in Python and data science libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow or PyTorch.
• Experience with building and deploying predictive models, experimentation frameworks, and statistical analyses.
• Strong knowledge of feature engineering, model evaluation, and optimisation techniques.
• Proficiency in SQL and experience with data warehouse technologies.
• Experience working with big data technologies (e.g., Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Ability to communicate complex analytical concepts to non-technical stakeholders.
• Strong problem-solving and critical thinking skills.
• Experience in building data pipelines and working with modern data engineering tools.

Preferred Qualifications

• Experience with graph databases (e.g., Ontotext, Stardog, Neo4j) and graph analytics.
• Experience with productionising ML models and MLOps practices.
• Familiarity with modern data visualisation tools (e.g., Power BI, Tableau, Looker).
• Knowledge of AI ethics, fairness, and responsible data use.
• Publications or contributions to open-source projects in data science or ML communities.
• Experience working in enterprise or applied research environment