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- Tasks: Design and deploy AI-driven solutions using AWS ML infrastructure for real-world impact.
- Company: Join a high-growth tech organisation leading the way in AI solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a tangible difference by scaling AI systems that transform enterprise workflows.
- Qualifications: 6-10+ years in data science, with hands-on AWS ML experience and strong Python skills.
- Other info: Be part of a forward-thinking team with exciting projects and a focus on innovation.
The predicted salary is between 54000 - 84000 £ per year.
PaymentGenes is proud to be partnering with a high-growth, international technology organisation to appoint a Senior / Lead Data Scientist (AI-Native, AWS ML Stack, Production-Focused). This is a strategic hire within a business scaling real-world AI solutions — moving beyond experimentation into production-grade, AI-powered systems embedded directly into enterprise workflows.
If you are passionate about deploying scalable ML systems that deliver measurable commercial impact, this opportunity is for you. You will design, deploy, and scale AI-driven solutions using modern foundation models and AWS-native machine learning infrastructure.
- Model Development & AI Systems Design
- Design and train predictive models using AWS SageMaker
- Develop LLM-powered systems via AWS Bedrock (including Claude integration)
- Build RAG pipelines combining structured and unstructured data
- Architect reasoning agents using advanced foundation models
- Orchestrate multi-step AI workflows
- Deploy AI-powered decision layers into enterprise processes
- Design human-in-the-loop feedback systems to improve performance
- Leverage Bedrock for foundation model access
- Utilise S3, Glue, Athena for data processing
- Implement CI/CD for ML workflows
- Monitor performance via CloudWatch and drift detection tooling
- Build reproducible ML pipelines
- Monitor performance and trigger retraining cycles
- Identify high-impact AI use cases
- Translate business problems into ML system designs
- Lead experimentation frameworks (A/B testing, uplift modelling)
- Mentor data scientists and collaborate closely with data engineering
- Communicate AI strategy and risk to senior stakeholders
- Advanced Python & SQL
- Statistical modelling & ML algorithms
- AWS ML Stack
- SageMaker (training, endpoints, pipelines)
- Bedrock (foundation models incl. AI-Native Tooling)
- Foundation models for reasoning workflows
6–10+ years in data science or applied ML. Proven experience deploying ML models into production. Hands-on experience with AWS-native ML services. Experience building LLM-powered workflows or AI agents. AI governance awareness. A bias toward practical deployment over research-only outputs.
- Example Projects You Might Deliver
- Production fraud detection model deployed via SageMaker endpoint
- Internal AI copilot powered by Bedrock and embedded into workflows
- RAG-based compliance monitoring assistant
- AI-driven document intelligence system (classification + extraction)
This is a rare opportunity to build AI systems that operate at real scale within a forward-thinking technology environment. If this excites you, please reach out to the PaymentGenes team directly via LinkedIn, who can provide more detail and insight into this company's journey into AI and the opportunity.
Lead / Senior Data Scientist - AI / ML in City of London employer: PaymentGenes
Contact Detail:
PaymentGenes Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead / Senior Data Scientist - AI / ML in City of London
✨Tip Number 1
Network like a pro! Connect with people in the industry on LinkedIn, attend meetups, and join relevant online communities. The more you engage, the better your chances of hearing about opportunities before they even hit the job boards.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS ML Stack or AI systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical stakeholders.
✨Tip Number 4
Don't forget to apply through our website! We have a range of exciting roles that could be perfect for you. Plus, applying directly shows your enthusiasm and commitment to joining our team.
We think you need these skills to ace Lead / Senior Data Scientist - AI / ML in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the job description. Highlight your experience with AWS ML Stack, model development, and any relevant projects you've worked on. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about deploying scalable ML systems and how your background makes you a perfect fit for this role. Let us know what excites you about working with AI solutions in a production environment.
Showcase Your Projects: If you've worked on any relevant projects, especially those involving AWS services or AI-driven solutions, make sure to mention them. We love seeing real-world applications of your skills, so don’t hold back on sharing your achievements!
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 this exciting opportunity. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at PaymentGenes
✨Know Your Tech Stack
Make sure you’re well-versed in the AWS ML stack, especially SageMaker and Bedrock. Brush up on how to design and deploy predictive models, as well as your experience with CI/CD for ML workflows. Being able to discuss specific projects where you've used these tools will really impress.
✨Showcase Real-World Impact
Prepare examples of how your work has delivered measurable commercial impact. Whether it’s a fraud detection model or an AI copilot, be ready to explain the business problems you solved and the results achieved. This shows you understand the importance of practical deployment over theoretical knowledge.
✨Communicate Clearly
You’ll need to communicate complex AI strategies to senior stakeholders, so practice explaining your projects in simple terms. Use clear, concise language and avoid jargon unless necessary. This will demonstrate your ability to bridge the gap between technical and non-technical audiences.
✨Be Ready to Mentor
As a Lead Data Scientist, mentoring is part of the role. Think about how you’ve supported junior data scientists in the past and be prepared to discuss your mentoring style. Highlight any frameworks or practices you’ve implemented to help others grow in their roles.