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- Tasks: Design and deploy AI-driven solutions using AWS ML stack for real-world impact.
- Company: Join a high-growth, international tech organisation leading in AI solutions.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Why this job: Make a tangible difference by scaling AI systems that transform business operations.
- Qualifications: 6-10+ years in data science with hands-on AWS ML experience.
- Other info: Exciting projects with measurable outcomes in a forward-thinking environment.
The predicted salary is between 48000 - 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. This role goes beyond model experimentation! You will design, deploy, and scale AI-driven solutions using modern foundation models and AWS-native machine learning infrastructure. From LLM-powered agents to predictive models embedded in automated workflows, your work will directly influence business operations at scale. You’ll operate at the intersection of modelling, engineering, and intelligent automation.
What You’ll Do
- 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
- Develop evaluation frameworks for accuracy, bias, and robustness
- Apply best practices in feature engineering and experimentation
- AI Agent & Workflow Integration
- Architect reasoning agents using advanced foundation models
- Use code-generation tooling for automation logic and integration scripting
- Orchestrate multi-step AI workflows
- Deploy AI-powered decision layers into enterprise processes
- Design human-in-the-loop feedback systems to improve performance
- AWS ML Infrastructure
- Deploy and manage models using SageMaker (training, endpoints, pipelines)
- Leverage Bedrock for foundation model access
- Implement serverless inference with Lambda & API Gateway
- Utilise S3, Glue, Athena for data processing
- Implement CI/CD for ML workflows
- Monitor performance via CloudWatch and drift detection tooling
- Optimise inference cost and latency
- Productionisation & MLOps
- Build reproducible ML pipelines
- Implement model versioning and dataset tracking
- Design structured output validation and guardrails
- Monitor performance and trigger retraining cycles
- Ensure governance, compliance, and security alignment
- Business Impact & Leadership
- 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
Technical Environment
- Core Data Science
- Advanced Python & SQL
- Statistical modelling & ML algorithms
- Feature engineering
- Experiment design & evaluation
- AWS ML Stack
- SageMaker (training, endpoints, pipelines)
- Bedrock (foundation models incl. Claude)
- Lambda (serverless inference)
- S3, Glue, Athena
- CloudWatch
- IAM & security best practices
- AI-Native Tooling
- Foundation models for reasoning workflows
- Code-generation tooling for automation scripting
- Agent orchestration frameworks
- Enterprise workflow automation tools
- RAG architectures
- Embeddings & vector stores
What We’re Looking For
- 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
- Demonstrated delivery of measurable business impact
You’ll Thrive If You Have:
- Strong problem-framing ability
- Systems-level thinking beyond model accuracy
- AI governance awareness
- Clear communication across technical and executive audiences
- 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
- Automated revenue forecasting pipeline with retraining triggers
- AI-driven document intelligence system (classification + extraction)
What Success Looks Like
- Reduced time from model prototype to production
- Stable, monitored ML endpoints delivering measurable ROI
- Improved decision accuracy in automated workflows
- Strong adoption of AI-enabled tools across the business
- Controlled infrastructure cost per inference
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.
Senior / Lead Data Scientist in London employer: PaymentGenes
Contact Detail:
PaymentGenes Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior / Lead Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS ML Stack and AI systems. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss how you've deployed ML models in production and the impact they had on business outcomes.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Senior / Lead Data Scientist in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AWS ML services and AI systems. We want to see how your skills align with the role, so don’t hold back on showcasing your relevant projects!
Showcase Your Impact: When detailing your past experiences, focus on the measurable outcomes of your work. We love to see how you've delivered real business impact through your data science projects, so include those success stories!
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain your technical skills and experiences, as we appreciate clarity over jargon. Remember, we want to understand your journey easily!
Apply Through Our Website: We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity. Don’t miss out!
How to prepare for a job interview at PaymentGenes
✨Know Your Tech Stack
Familiarise yourself with the AWS ML stack, especially SageMaker and Bedrock. Be ready to discuss how you've used these tools in past projects, focusing on deployment and productionisation of models.
✨Showcase Real-World Impact
Prepare examples of how your work has delivered measurable business impact. Highlight specific projects where you’ve deployed ML models that improved decision-making or operational efficiency.
✨Demonstrate Systems Thinking
Be prepared to discuss how you approach problem framing and system-level thinking. Think about how you can translate complex business problems into effective ML solutions and articulate this clearly.
✨Engage with AI Governance
Understand the importance of AI governance and compliance. Be ready to talk about how you ensure security and ethical considerations in your ML workflows, as this is crucial for any production-focused role.