Lead Data Scientist

About Hiscox Re & ILS

Hiscox Re & ILS is located in Bermuda and London and underwrites reinsurance risks from around the world on behalf of our own and other people’s capital. The team of over 100 smart, enthusiastic individuals are committed to providing top quality service to brokers and clients across a broad range of reinsurance products. Our approach is simple; match the best underwriting risk against globally diversified capital. Our proactive, product-led underwriting approach is complemented by a commitment to world class service and purpose-built analytics and systems. We know our people are our biggest asset so finding and retaining the best talent is critical to achieving our vision.

Key Responsibilities

1. Chapter Leadership & Talent Management

• People Management: Lead, recruit, mentor, coach, train, and retain a high-performing chapter of Data Scientists and Machine Learning Engineers (expected to grow to over the next few years), including both Hiscox FTE and Partner colleagues. Inspire chapter members to be curious, embrace new ideas, and nurture psychological safety to help build a high-performance culture.
• In this role you will manage at least one to two Hiscox data scientists and some partner resources too. You will be expected to manage their performance with relevant PDRs and help to calibrate their overall performance ratings with other people managers in the team. You will work with the Head of Data and Group Head of Data Science to help tailor the training needs to suit each individual and ensure they have the opportunity to grow and progress in their chosen career paths.
• Culture & Development: Cultivate a collaborative, engaged, and fulfilled team culture. Provide technical leadership, career guidance, and direction to the team, ensuring successful delegation to all chapter members.
• Best Practice: Ensure the chapter adheres to best practices in data science, AI, and complex analytics, sharing knowledge across the wider Hiscox Analytics and Data community through active participation in Communities of Practice.
• Mentoring: Mentor and educate others in the various data science techniques, encouraging the team to continually learn – especially supporting the adoption of LLMs and proprietary models that could significantly accelerate our ability to deploy value.

2. Solution Delivery & ML Ops Excellence

• Hands-On Architecture: Get hands-on to help architect, design, and build complex models that integrate into existing IT solutions to improve data-driven decision-making throughout the Reinsurance Value Chain. Ensure you and your chapter members work closely with Platform Engineers and Architects to create the most optimal and safe solutions.
• ML Ops Platform: Actively work with the Head of Platform Engineering to establish and maintain a professional ML Ops platform to facilitate the effective support of developed models in production. This includes embedding a rigorous model evaluation process, ensuring consistent model refresh/rebuild capabilities due to model or data drift, and implementing effective monitoring and alerts against rogue behaviour.
• Speed of Execution: Empower the chapter to work effectively within autonomous squads, delivering desired and valuable outcomes in an incremental manner while correctly balancing accuracy, fast return on investment, and reliability. Understand when to move on and fail fast – as a lot of AI use cases will be highly experimental in nature with no guarantees of success.
• Pipeline Collaboration: Work with Data Engineering and other teams to properly understand and contribute to the requirements and building of necessary data pipelines/products consumed by our models.

3. Strategy, Governance & Innovation

• Use Case Identification: Continually work with key business leaders, the Head of Data and key stakeholders to identify new use cases and opportunities where data science/AI can deliver a competitive advantage or reduce waste/costs through efficiency gains.
• Governance & Compliance: Champion data and model governance, working within the Group AI Governance Framework. Ensure a mature understanding of data governance, lineage, and version control, and enforce data privacy by design in all new work.
• Innovation: Maintain a strong awareness of new trends and techniques in Data Science, AI, and Machine Learning. Drive the identification of innovative data solutions and technologies, and actively undertake Proof of Concepts and R&D to deliver reliable insights and potential next steps quickly.
• Communication: Ensure clear and effective communication of data science solutions and outcomes across business layers, technical teams, and senior leadership.

Required Experience and Structure

• Reporting: Reports into the RE&ILS Head of Data with a dotted line into the new Group Head of Data Science.
• Direct Reports: Direct line management for Data Scientists and Machine Learning Engineers within the RE&ILS Data Science Chapter.
• Key Partners: RE&ILS CTO, Group Head of Data Science, RE&ILS Head of Data, Product Managers, Product Owners, and strategic partners (e.g. Google Cloud, Microsoft).

Candidate Profile

The successful candidate will be an energetic, highly capable, and passionate Senior Data Scientist who wants to move into their first people management role. You will need to balance technical expertise with effective people management and strategic business partnership. You must be comfortable working in an FCA-regulated business, advocating for a data-driven approach, and driving innovation while maintaining rigorous AI governance standards. You will have a strong determination and resilience and not fear failure or the inevitable setbacks often found in highly experimental work.

Main experience required is:

• Deep Interest in Leadership: As an experienced data scientist or machine learning engineer, has previously demonstrated excellent traits in mentoring and coaching more junior team members and has been happy to invest in them - clearly demonstrating they care about other peoples’ career progression. Also you can evidence some successful business outcomes that you personally drove forward using advanced Data Science/AI – as this most likely is your first leadership role.
• Technical Depth: Extensive "hands-on" experience in architecting, designing, and building complex ML models, with a strong understanding of cloud services and the AI/ML/LLM ecosystem.
• ML Ops Maturity: Proven ability to build, maintain, and support production-deployed models, including establishing effective monitoring, evaluation, and refresh processes.
• AI and Data Governance: Mature understanding of AI and Data governance - data obfuscation & data privacy laws, data version control, data lineage, and the application of AI governance frameworks in a regulated environment.
• Stakeholder Management: Experience in managing and driving collaboration with business unit leaders (Underwriters, pricing teams, MI team) and external technology partners like Google Cloud, Snowflake, DataIku and Microsoft.
• Education: Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent.
• Capacity for independent, high-quality research and exploratory data analysis, with the ability to develop and apply novel methodologies to solve ill-defined and challenging business problems.
• Exceptional proficiency in Python (and R) for data science, coupled with SQL capabilities for data manipulation and extraction. Demonstrable mastery in a wide range of machine learning and statistical modelling techniques, from classical linear models and tree-based methods to advanced deep learning architectures.
• Real world experience with, or demonstrable capability for, applications of Generative AI, Large Language Models (LLMs), AI Agents and related areas such as Natural Language Processing (NLP) or Computer Vision, relevant to business solutions.
• Demonstrate a Solid understanding of foundational statistics and experimental design. Pioneer the development and robust deployment of highly impactful machine learning and AI-driven solutions, directly contributing to significant commercial value, quantifiable business growth, or efficiency gains across the organization.
• Ability to translate complex data science insights into tangible, measurable improvements across critical business processes and key performance indicators, ensuring the real-world adoption and beneficial outcomes of our analytical work.
• Ability to elevate the technical maturity and capabilities of the data science chapter by shaping and implementing industry-leading AI, data, and analytics techniques, methodologies, and engineering best practices.
• Educator: shown the ability to train and coach more junior and mid-level team members - ensuring they have excellent career progression and become high performers.

If you are a visionary expert looking for your first leadership role and ready to accelerate our value delivery and champion all things Data Science and AI, we invite you to apply for the position of Lead Data Scientist at Hiscox Re & ILS. Join our team and be a catalyst for AI adoption in a dynamic, forward-thinking organization.

Work with amazing people and be part of a unique culture