Data Scientist

Data Scientist - Reinforcement Learning

We’re hiring a Data Scientist with hands-on Reinforcement Learning experience to help design and operate pricing and decisioning systems used in live, production environments.

This role focuses on applying RL to real optimisation problems, where models directly influence outcomes and must perform under real-world constraints such as noisy data, delayed rewards, and system trade-offs. This is not a research-only role-production impact matters.

What You’ll Be Working On

• Designing, training, and improving reinforcement learning models for pricing and decision optimisation
• Applying ML to sequential decision-making and optimisation problems
• Deploying, operating, and iterating on models in production environments
• Using AWS ML services including SageMaker and Bedrock
• Partnering closely with data engineering and product teams to integrate models into live systems
• Monitoring model performance and improving behaviour based on real-world feedback

Core Technology Stack

• Machine Learning: Reinforcement Learning, decision-focused ML
• Cloud ML: AWS SageMaker, AWS Bedrock
• Programming: Python

What We’re Looking For

• Demonstrated experience applying reinforcement learning in production systems
• Strong applied ML skills with a focus on decisioning, optimisation, or control problems
• Solid Python development skills
• Experience deploying, maintaining, and iterating on production ML models
• Comfort working with imperfect data, evolving requirements, and real-world constraints

Nice to Have

• Experience with dynamic pricing, auctions, or optimisation systems
• Background in building ML systems that operate at scale
• Experience working closely with data engineering teams on end-to-end ML pipelines

Why This Role

• Work on live systems where models directly impact outcomes
• High ownership from model design through to production performance
• Meaningful, technically challenging optimisation problems
• Opportunity to apply reinforcement learning beyond theory and experimentation

If you’re excited by seeing reinforcement learning models operate in the real world—and improving them over time-this role is well worth exploring.