Data Scientist with c.4 years' commercial experience delivering machine learning solutions within regulated environments. His expertise spans the full ML lifecycle, from feature engineering and model development through to deployment and monitoring in production. Whilst his experience has been gained in financial services, his technical skillset in Generative AI, NLP and cloud-native machine learning is highly transferable across sectors.

Key Skills:

• Strong experience building and deploying end-to-end machine learning solutions, covering data ingestion, feature engineering, model training, validation and production deployment.

• Advanced Python and cloud expertise, including GCP, Vertex AI, Kubeflow, BigQuery and containerised ML workflows.

• Deep knowledge of modern AI technologies such as LLMs, NLP, RAG architectures, prompt engineering, LangChain and Hugging Face.

• Effective communicator with experience partnering across technical and business teams to translate analytical outputs into actionable insights.

Key Impacts:

• Developed predictive machine learning models that improved risk assessment and fraud detection capabilities by 20%, enhancing business decision-making and operational effectiveness.

• Automated data pipelines and workflows using Python and SQL, reducing manual data preparation effort by 35% and enabling faster analytics.

• Designed claims prediction models that reduced manual review requirements by 35%, improving consistency and scalability across high-volume operations.

• Improved data retrieval performance by 30% and reduced model training times by 25% through optimisation of cloud-based ML workflows and infrastructure.