As a Senior Data Scientist, the candidate will work closely with Product and Engineering teams and will play a significant role in the team responsible for building the AI and Analytics capabilities that power the Insurwave platform. The team is self-sufficient and fully responsible for design, development, testing, delivery, and support of the solutions. The candidate will be working across the full ML development lifecycle: data wrangling, model build, model evaluation, model deployment, and model monitoring. The candidate will actively participate in these processes and will be leading and making technology and design decisions. The candidate will build solutions aligned with company-wide rules of engagement and standards and will work closely with the Head of Data and AI to improve them when needed. The candidate will support team members' growth and promote an open, learning culture.

Responsibilities

• Lead and manage complex data science projects from conception to deployment, including defining project scope, timelines, and deliverables.
• Build high-performing AI/ML models that meet business-defined performance metrics, ensuring scalability, efficiency, and reliability.
• Develop and deploy production-ready data science code and models using fully automated processes, including Continuous Integration/Continuous Deployment (CI/CD) and testing frameworks.
• Continuously improve the performance, security, architecture, and maintainability of owned services through iterative development and optimization.
• Work closely with data analysts, data engineers, data scientists, and other business areas to ensure solutions are aligned with requirements, delivered according to plans, and developed to expected quality and security standards.
• Work closely with the AI product manager to review model monitoring reports and analyze datasets in order to inform model improvement needs.
• Provide technical leadership and mentorship to junior data scientists, fostering a culture of learning, collaboration, and continuous improvement.
• Ensure the team adheres to defined best practices, standards, and processes, promoting excellence in technical execution and project delivery.
• Stay current with the latest advancements in data science and machine learning research and propose innovative solutions to address business challenges.

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