Lead Data Scientist Job Central London area,City Of London England UK,IT/Tech

Location: City Of London

Job Type: Permanent

Reporting to: RE&ILS Head of Data

Location: London

Role Overview: The Lead Data Scientist is a crucial member of the extended RE&ILS Tech Leadership Team, responsible for building, leading, and nurturing a highly capable (New) Data Science and Machine Learning chapter. This role facilitates the successful delivery of new AI Cloud-based solutions against our growing list of business use cases (involving complex Data Analytics, Machine Learning, and AI Agent based solutions).

You will co‑lead the new Data Science chapter with the RE&ILS Head of Data and help ensure its members provide Data Science capabilities and insights to support decision‑making across RE&ILS strategic Value Streams. Whilst the chapter grows, you are expected to be hands‑on actively helping to architect, design, and build AI or Data Science‑based solutions, using internally trained models, LLMs and/or a combination of both.

Whilst undertaking this pioneering work it is essential that you and your entire chapter operates within the boundaries of the Group AI Governance Framework and maintains rigorous data governance standards. You will be accountable for this adherence. This is likely to be your first managerial role and the first time you have had one or more direct reports – having previously been a Senior Data Scientist or Machine Learning Engineer yourself.

Key Responsibilities

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.
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.

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.
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.
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.
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.

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…