Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead a dynamic team in building AI-driven solutions and advanced analytics.
- Company: Global insurance and reinsurance leader with a focus on technology innovation.
- Benefits: Competitive salary, career development, and a collaborative work culture.
- Other info: Exciting opportunities for growth in a fast-paced, innovative environment.
- Why this job: Shape the future of AI while making a real impact in a thriving industry.
- Qualifications: Hands-on experience in machine learning and strong leadership skills.
The predicted salary is between 80000 - 100000 £ per year.
This is a key leadership role within a global insurance and reinsurance business, sitting as part of a wider technology leadership team. The position is focused on building and leading a growing Data Science & Machine Learning function, while delivering impactful AI-driven solutions across the organisation. You’ll play a central role in shaping and scaling AI capabilities, working on a range of use cases spanning advanced analytics, machine learning, and emerging areas such as Generative AI and AI agents. Alongside leadership responsibilities, this role remains hands-on, with involvement in architecting, designing, and delivering production-grade data science solutions using a mix of proprietary models, cloud platforms, and LLM-based approaches.
Key Responsibilities:
- Leadership & Team Development: Build, lead, and mentor a growing team of Data Scientists and ML Engineers; Take ownership of performance, development, and engagement across the team; Foster a strong, collaborative, and high-performing team culture; Support hiring and help shape the future structure of the function.
- Technical Delivery & Architecture: Remain hands-on in designing and building ML/AI solutions end-to-end; Work closely with engineering teams to deploy scalable, production-ready models; Contribute to the development of robust MLOps practices, including monitoring, evaluation, and model lifecycle management; Partner with data teams to define and build the data pipelines required for advanced analytics.
- Strategy, Innovation & Stakeholder Engagement: Work with senior stakeholders to identify high-impact AI use cases; Translate complex business problems into data science solutions; Drive adoption of modern AI techniques, including LLMs and advanced ML methods; Communicate insights and solutions clearly to both technical and non-technical audiences.
- Governance & Best Practice: Ensure all AI and data solutions align with internal governance and regulatory standards; Promote best practices in data science, experimentation, and model development; Maintain a strong focus on data privacy, model transparency, and ethical AI use.
Desired experience includes:
- Strong hands-on experience building and deploying machine learning models in production.
- Solid understanding of cloud-based AI/ML ecosystems and modern data platforms.
- Experience working with LLMs, Generative AI, or NLP/computer vision use cases.
- Knowledge of MLOps practices, including CI/CD, monitoring, and model lifecycle management.
- Excellent stakeholder management and communication skills.
- Passion for mentoring and developing others.
Experience in regulated environments (e.g. financial services or insurance) is beneficial but not essential.
Lead Data Scientist employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist
✨Tip Number 1
Network like a pro! Reach out to connections in the industry, attend meetups, and engage on platforms like LinkedIn. We can’t stress enough how personal connections can lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving machine learning and AI. This gives potential employers a taste of what you can do, and we love seeing hands-on experience.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate with both techies and non-techies. We want you to shine!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team and contributing to our exciting projects.
We think you need these skills to ace Lead Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Lead Data Scientist role. Highlight your hands-on experience with machine learning models and any leadership roles you've had, as we’re looking for someone who can build and mentor a team.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and data science, and how your background makes you the perfect fit for our team. Don’t forget to mention any relevant projects or achievements!
Showcase Your Technical Skills: In your application, be sure to highlight your technical expertise, especially in cloud-based AI/ML ecosystems and MLOps practices. We want to see how you’ve applied these skills in real-world scenarios, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our awesome team at StudySmarter!
How to prepare for a job interview at SPG Resourcing
✨Know Your Stuff
Make sure you brush up on your technical skills, especially around machine learning models and cloud platforms. Be ready to discuss your hands-on experience with LLMs and Generative AI, as well as any relevant projects you've worked on.
✨Show Your Leadership Skills
Since this role involves building and mentoring a team, be prepared to share examples of how you've led teams in the past. Talk about your approach to fostering a collaborative culture and how you’ve supported team development.
✨Communicate Clearly
You’ll need to translate complex data science concepts for both technical and non-technical stakeholders. Practice explaining your past projects in simple terms, focusing on the impact and value they brought to the business.
✨Align with Governance Standards
Familiarise yourself with best practices in data privacy and ethical AI. Be ready to discuss how you ensure compliance with internal governance and regulatory standards in your previous work, as this will be crucial in the interview.