Senior Data Scientist / AI Solutions Engineer

Your Responsibilities Business Impact & Analytics

• Partner with underwriting and business stakeholders to address strategic challenges, such as portfolio optimization, risk assessment, loss trend analysis, and customer profitability.
• Translate business requirements into analytical frameworks and actionable insights.
• Drive data-informed decision-making through advanced analytics and AI solutions.

Data Science & AI Development

• Design, develop, and deploy machine learning and AI models for production environments.
• Apply techniques such as predictive modeling, NLP, classification, clustering, recommendation systems, and anomaly detection.
• Continuously improve model performance, monitoring, and governance practices.

Data & Platform Engineering

• Build and maintain scalable data pipelines using cloud-native technologies.
• Integrate structured and unstructured data from internal and external sources.
• Optimize data processing workflows and ensure the reliability of analytical platforms in production.

Product Development

• Develop end-to-end analytical applications, from proof-of-concept to enterprise-scale deployment.
• Collaborate closely with product managers, engineers, and business users to deliver high-value solutions.
• Contribute to enhancing user experience and increasing adoption of analytical tools across the organization.

Leadership & Collaboration

• Mentor junior data scientists and engineers.
• Promote best practices in analytics, AI, software development, and data engineering.
• Work within a highly collaborative, international team focused on innovation and continuous improvement.

What You Bring Required Qualifications

• Degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related field.
• Several years of professional experience in Data Science, AI Engineering, Machine Learning, or Data Engineering.
• Strong programming skills in Python and experience with SQL.
• Experience working with large-scale data processing frameworks such as Spark, Databricks, or similar technologies.
• Solid understanding of machine learning lifecycle management and software engineering best practices.
• Experience building and deploying production-ready analytical solutions.
• Strong analytical thinking and problem-solving abilities.
• Excellent communication skills and ability to engage with both technical and business stakeholders.
• Fluent English, written and spoken.

Preferred Qualifications

• Experience with Azure, AWS, or Google Cloud platforms.
• Hands-on experience with MLOps practices, model deployment, and monitoring.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with NLP, Generative AI, Large Language Models (LLMs), or Retrieval Augmented Generation (RAG).
• Experience with geospatial analytics or alternative data sources.
• Exposure to front-end technologies and data visualization frameworks.
• Experience in insurance, reinsurance, financial services, or risk management environments.

Why Join the Company?

• Work on high-impact projects that influence global underwriting and portfolio strategy.
• Collaborate with a diverse team of analytics, engineering, and insurance experts.
• Access cutting-edge cloud, AI, and data technologies.
• Enjoy opportunities for professional growth, continuous learning, and career development.
• Be part of an organization that values innovation, collaboration, and customer-centric thinking.

Your Responsibilities Business Impact & Analytics

• Partner with underwriting and business stakeholders to address strategic challenges, such as portfolio optimization, risk assessment, loss trend analysis, and customer profitability.
• Translate business requirements into analytical frameworks and actionable insights.
• Drive data-informed decision-making through advanced analytics and AI solutions.

Data Science & AI Development

• Design, develop, and deploy machine learning and AI models for production environments.
• Apply techniques such as predictive modeling, NLP, classification, clustering, recommendation systems, and anomaly detection.
• Continuously improve model performance, monitoring, and governance practices.

Data & Platform Engineering

• Build and maintain scalable data pipelines using cloud-native technologies.
• Integrate structured and unstructured data from internal and external sources.
• Optimize data processing workflows and ensure the reliability of analytical platforms in production.

Product Development

• Develop end-to-end analytical applications, from proof-of-concept to enterprise-scale deployment.
• Collaborate closely with product managers, engineers, and business users to deliver high-value solutions.
• Contribute to enhancing user experience and increasing adoption of analytical tools across the organization.

Leadership & Collaboration

• Mentor junior data scientists and engineers.
• Promote best practices in analytics, AI, software development, and data engineering.
• Work within a highly collaborative, international team focused on innovation and continuous improvement.

What You Bring Required Qualifications

• Degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related field.
• Several years of professional experience in Data Science, AI Engineering, Machine Learning, or Data Engineering.
• Strong programming skills in Python and experience with SQL.
• Experience working with large-scale data processing frameworks such as Spark, Databricks, or similar technologies.
• Solid understanding of machine learning lifecycle management and software engineering best practices.
• Experience building and deploying production-ready analytical solutions.
• Strong analytical thinking and problem-solving abilities.
• Excellent communication skills and ability to engage with both technical and business stakeholders.
• Fluent English, written and spoken.

Preferred Qualifications

• Experience with Azure, AWS, or Google Cloud platforms.
• Hands-on experience with MLOps practices, model deployment, and monitoring.
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with NLP, Generative AI, Large Language Models (LLMs), or Retrieval Augmented Generation (RAG).
• Experience with geospatial analytics or alternative data sources.
• Exposure to front-end technologies and data visualization frameworks.
• Experience in insurance, reinsurance, financial services, or risk management environments.

Why Join the Company?

• Work on high-impact projects that influence global underwriting and portfolio strategy.
• Collaborate with a diverse team of analytics, engineering, and insurance experts.
• Access cutting-edge cloud, AI, and data technologies.
• Enjoy opportunities for professional growth, continuous learning, and career development.
• Be part of an organization that values innovation, collaboration, and customer-centric thinking.