Risk Modelling Scientist

Overview

Are you a highly skilled Data Scientist ready to apply your advanced analytical and machine learning expertise to the complex challenges of the Life and Non-Life Insurance industry? A leading provider of Tailored Risk Solutions is seeking an innovative Data Scientist to join our client’s Business Intelligence team. This is a key position where you will leverage large-scale data to drive insights, inform critical decision-making, and enhance our core insurance operations.

This is your chance to build and ope rationalise predictive models that unearth hidden insights and directly improve business performance.

• Advanced Modelling:
  Develop, implement, and validate cutting‑edge machine learning algorithms and statistical models. Your primary goal will be to build and ope rationalise predictive models to enhance our insurance operations.
• Data Strategy & Insights:
  Analyze complex datasets to identify crucial trends, patterns, and correlations. You will generate, test, and interpret working hypotheses to provide actionable insights that address business challenges.
• Engineering & Automation:
  Build scalable data pipelines and infrastructure for collecting, processing, and analysing large volumes of structured and unstructured data. You will also automate recurring processes and monitor their efficiency.
• BI Solution Development:
  Develop comprehensive BI solutions utilizing the Microsoft BI Stack, including SQL, ETL scripting, database programming, and reporting tools.

• A minimum of 4 years’ working experience as a Data Scientist or in a similar role, preferably within the life and non‑life insurance industry.

• A Bachelor’s Degree/Diploma in Informatics, Computer Science, Statistics, Mathematics, or Information Technology.

• Proficiency in programming languages such as Python, R, or Java, C++, or C#.
• Extensive experience with T‑SQL and Microsoft SQL Server.
• Essential Azure Stack
  
  Experience:
  
  Power BI, Azure Data Factories, and Azure Synapse Analytics.
• Experience with data analysis and machine learning libraries (e.g., Tensor Flow, PyTorch, scikit‑learn).
• Experience with big data technologies (e.g., Hadoop, Spark, Kafka) and cloud platforms (AWS, Azure, Google Cloud Platform).

• Strong understanding of statistical concepts, data modelling techniques, and the ability to translate business requirements into actionable insights.
• Must have excellent problem‑solving skills and effective communication for presenting complex ideas.