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- Tasks: Design and deploy AI-driven solutions using AWS, impacting real-world business operations.
- 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 impact by scaling innovative AI systems in a dynamic environment.
- Qualifications: 6-10+ years in data science, with hands-on AWS ML experience.
- Other info: Mentor fellow data scientists and collaborate on cutting-edge projects.
The predicted salary is between 70000 - 90000 £ 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- 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
- Architect reasoning agents using advanced foundation models
- Use code-generation tooling for automation logic and integration scripting
- Design human-in-the-loop feedback systems to improve performance
- Deploy and manage models using SageMaker (training, endpoints, pipelines)
- 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
- 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
- Identify high-impact AI use cases
- Translate business problems into ML system designs
- Mentor data scientists and collaborate closely with data engineering
- Communicate AI strategy and risk to senior stakeholders
- Core Data Science
- Advanced Python & SQL
- Statistical modelling & ML algorithms
- AWS ML Stack
- SageMaker (training, endpoints, pipelines)
- Lambda (serverless inference)
- 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
- Embeddings & vector stores
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
- Internal AI copilot powered by Bedrock and embedded into workflows
- RAG-based compliance monitoring assistant
- Automated revenue forecasting pipeline with retraining triggers
- 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 AI-Native Data Scientist - Production ML on AWS employer: PaymentGenes
Contact Detail:
PaymentGenes Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI-Native Data Scientist - Production ML on AWS
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working at companies you're interested in. A friendly chat can open doors and give you insider info that could help you stand out.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AWS and ML. This is your chance to demonstrate your hands-on experience and how you've tackled real-world problems.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Be ready to discuss your past projects and how they delivered measurable impact. Remember, it's not just about what you did, but how it benefited the business!
✨Tip Number 4
Apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight your passion for deploying scalable ML systems and how you can contribute to our mission.
We think you need these skills to ace Senior AI-Native Data Scientist - Production ML on AWS
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role! Highlight your experience with AWS, ML models, and any relevant projects that showcase your skills in deploying AI solutions. We want to see how you can bring measurable impact!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background aligns with our mission at StudySmarter. Be sure to mention specific experiences that relate to the job description.
Showcase Your Technical Skills: Don’t hold back on your technical prowess! Clearly outline your experience with Python, SQL, and AWS services like SageMaker and Lambda. We’re looking for candidates who can hit the ground running, so make it easy for us to see your qualifications.
Apply Through Our Website: We encourage you to apply through our website for a smoother process! It helps us keep track of applications and ensures you don’t miss out on any important updates. Plus, it’s super easy to do!
How to prepare for a job interview at PaymentGenes
✨Know Your AWS Inside Out
Make sure you’re well-versed in AWS services, especially SageMaker and Lambda. Brush up on how to deploy and manage ML models using these tools, as you'll likely be asked about your hands-on experience with them during the interview.
✨Showcase Your Real-World Impact
Prepare examples of how your previous work has delivered measurable business impact. Be ready to discuss specific projects where you’ve designed and deployed AI solutions that improved decision-making or operational efficiency.
✨Master the Art of Communication
Since this role involves communicating AI strategies to senior stakeholders, practice explaining complex technical concepts in simple terms. Think about how you can convey your ideas clearly and effectively to both technical and non-technical audiences.
✨Demonstrate Problem-Solving Skills
Be prepared to tackle hypothetical scenarios related to AI deployment and model performance. Show your systems-level thinking by discussing how you would approach real-world challenges, such as bias detection or model retraining cycles.