Data Scientist - Insurance Analytics

About the Role

Our client is seeking a skilled and analytical Data Scientist to join their innovative analytics team in Edinburgh . In this role, you will leverage advanced statistical and machine learning techniques to extract actionable insights from complex insurance datasets. Your work will directly influence product development, pricing strategies, risk assessment, and customer experience initiatives. The ideal candidate is passionate about data, possesses strong programming skills, and thrives in a collaborative environment where data-driven decision-making is paramount. You will contribute to solving challenging problems and driving the company's competitive edge through sophisticated data analysis and predictive modelling.

Key Responsibilities

• Develop and implement predictive models and machine learning algorithms to forecast insurance claims, identify fraud, and optimize pricing.
• Analyse large, complex datasets to uncover trends, patterns, and insights related to customer behaviour, risk, and market dynamics.
• Design and conduct A/B tests and other experiments to evaluate the effectiveness of business strategies.
• Create compelling data visualisations and dashboards to communicate findings to business stakeholders.
• Collaborate with product managers, actuaries, and engineers to integrate data-driven solutions into business processes.
• Stay current with the latest advancements in data science, machine learning, and AI.

Requirements

• Master's or Ph.D. in a quantitative field such as Data Science, Statistics, Computer Science, or a related discipline.
• Proven experience in applying data science and machine learning techniques to solve real-world problems, preferably in the insurance industry.
• Proficiency in programming languages like Python or R, and experience with relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Strong SQL skills and experience working with large databases.
• Excellent communication and presentation skills, with the ability to explain complex models to non-technical audiences.

Benefits

• Competitive salary and annual bonus.
• Hybrid working model promoting work-life balance.
• Comprehensive health and wellness programs.
• Generous paid time off and company holidays.
• Access to cutting-edge technology and opportunities for professional development.