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.