Principal Data Scientist – Machine Learning, AI

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Create machine learning and AI systems that enhance decision-making in areas such as pricing, underwriting, portfolio management, operations, and claims.

• Collaborate with structured data, textual information, documents, and external data sources, utilizing statistical modeling, contemporary machine learning, AI, and agentic workflows to address complex real-world challenges.

• Determine the most suitable approach, develop production-ready solutions, and assess the business impact of your contributions.

• Address a wide array of machine learning and AI issues including predictive modeling, classification, ranking, matching, recommendation, anomaly detection, information extraction, entity resolution, creation of high-quality datasets, and the automation of analytical and decision-making processes.

⛳️ Requirements

• A robust quantitative background in statistics, probability, optimization, or applied mathematics.

• Strong modeling judgment – you understand what it takes for a model to perform well in real-world scenarios, not just on a validation dataset.

• Proficient programming abilities.

• Genuine enthusiasm for working with LLMs and agentic AI as everyday tools, regardless of your current background.

• Effective communication skills with both technical and non-technical stakeholders – you can clarify a lift curve to an underwriter and a shrinkage prior to a statistician.

• Experience in one or more of the following areas is particularly advantageous: Proven track record with LLM-powered applications or AI agents, especially if you’ve engaged in the essential work of verifying their performance. Depth in the statistical toolkit beyond supervised prediction: hierarchical models, shrinkage estimation, causal inference and experimentation, survival analysis, extreme value theory, or demand and elasticity modeling.

• Knowledge of the insurance domain, including pricing, reserving, claims, underwriting, or distribution.

• Actuarial qualifications or background (partially or fully qualified).

• Experience in regulated industries where model governance and explainability are critical.

• ML engineering experience: transitioning models from research code to production services, or developing the tools and frameworks that assist others in deployment.

• Skills in cloud and infrastructure: AWS, Azure, or GCP; containers and orchestration; APIs and data pipelines designed with cost, latency, and reliability in mind.

• Practical experience in MLOps: experiment tracking, model monitoring, automated retraining, and CI/CD for models and agents.

🏝️ Benefits

• A variety of quantitative challenges across multiple domains.

• The opportunity to explore the rapidly changing landscapes of ML & AI, from gradient boosting and deep learning to foundation models and agentic systems, all while maintaining a focus on rigorous experimentation and measurable business impact.

• A collaborative team consisting of data scientists, engineers, actuaries, underwriters, and product managers who take pleasure in solving complex problems together.