Risk Analyst – Data Science & Analytics

We are looking for a Risk Analyst – Data Science & Analytics to join our Market Insights & Custom Analytics team. This is a hands-on analytics role focused on turning large and complex datasets into high-quality market intelligence, client-specific insights and analytical solutions. You will work across exploratory analysis, statistical modelling, segmentation, benchmarking, customer and portfolio analytics, and automation of repeatable insight workflows. The role requires strong coding and analytical depth together with the ability to explain what the data means for a business audience; prior experience in a specific risk domain is not mandatory. What you'll do Analyse large bureau and client datasets to identify market trends, portfolio patterns, customer segments, emerging risks and growth opportunities. Deliver custom analytics assignments by converting client questions into structured hypotheses, analytical methods, outputs and recommendations. Build statistical models, segmentations, benchmarks and diagnostic analyses where they materially improve the quality of insight or decision making. Perform data preparation, exploratory analysis, feature engineering and quality checks using efficient Python and SQL code. Develop repeatable analytical datasets, code libraries and automated workflows to improve the speed and consistency of recurring market-insight outputs. Create clear, decision-oriented charts, tables and narratives that communicate analytical findings without overstating what the data supports. Contribute analytical content to industry insight reports, client presentations and other thought-leadership outputs. Work with stakeholders to refine requirements, validate findings and ensure the final output addresses the intended business question. Maintain best practices for analytical accuracy, documentation, reproducibility and peer review. Follow applicable data-security, governance and compliance requirements. What success looks like Accurate, insightful and custom analytics and market-insight deliverables. Outputs that translate data into clear business implications and are useful to clients and internal stakeholders. Increasing automation and reuse across recurring reports, benchmarks and analytical workflows. Strong analytical quality and low rework through disciplined validation and documentation.

What you'll need to bring Approximately 3+ years of experience in analytics, data science, decision science, statistical modelling or a closely related field. Strong hands-on proficiency in Python for data manipulation, exploratory analysis, statistical modelling and machine learning. Strong working knowledge of SQL, including the ability to extract, transform and analyse large and complex datasets. Sound grounding in applied statistics, including sampling, distributions, hypothesis testing, regression and model evaluation. Hands-on experience with common supervised and unsupervised techniques such as regression, tree-based methods, ensemble methods, gradient boosting, classification, clustering and segmentation. Understanding of model-development practices including train/validation/test design, cross-validation, overfitting, performance metrics, stability and interpretability. Strong data-wrangling, data-quality assessment, feature-engineering and analytical validation skills. Convert a business question into a structured analytical approach and communicate findings clearly to technical and non-technical stakeholders. A disciplined approach to coding, documentation, reproducibility and quality assurance. Good to have SAS or another statistical programming environment. Git or similar version-control tools and collaborative coding practices such as peer review. Cloud analytics platforms, distributed computing or tools such as Spark / Databricks. Experience automating analytical workflows or developing reusable analytics libraries and utilities. Exposure to productionisation, model monitoring or model-governance frame