Risk Analyst

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

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.