Principal Data Scientist – Machine Learning, AI
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Job Description
Data Scientist at Accelerant develops machine learning and AI systems improving decisions in insurance. Collaborative role tackling various machine learning and AI challenges in pricing, underwriting, and operations.
Responsibilities:
- Develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations, and claims.
- Work across structured data, text, documents, and external data sources, applying statistical modeling, modern machine learning, AI and agentic workflows to solve challenging real-world problems.
- Identify the right approach, build production-ready solutions, and measure the business impact of your work.
- Tackle a broad range of machine learning and AI problems such as predictive modeling, classification, ranking, matching, recommendation, anomaly detection, information extraction, entity resolution, building high-quality datasets, and automating analytical and decision-making workflows.
Requirements:
- A strong quantitative foundation: statistics, probability, optimisation, or applied mathematics
- Sound modelling judgement - you know what it takes for a model to hold up in the real world, not just on a validation set
- Strong programming skills
- Real willingness to work with LLMs and agentic AI as everyday tools, wherever your background sits today
- Clear communication with both technical and non-technical audiences - you can explain a lift curve to an underwriter and a shrinkage prior to a statistician
- Experience in one or more of the following is especially valuable: Track record with LLM-powered applications or AI agents, especially if you've done the unglamorous work of proving they perform Depth in the statistical toolkit beyond supervised prediction: hierarchical models and shrinkage estimation, causal inference and experimentation, survival analysis, extreme value theory, or demand and elasticity modelling
- Insurance domain knowledge: pricing, reserving, claims, underwriting, or distribution
- Actuarial background or qualifications (partially or fully qualified)
- Experience in regulated industries where model governance and explainability matter
- ML engineering experience: taking models from research code to production services, or building the tooling and frameworks that help others deploy
- Cloud and infrastructure skills: AWS, Azure, or GCP; containers and orchestration; APIs and data pipelines built with cost, latency, and reliability in mind
- MLOps in practice: experiment tracking, model monitoring, automated retraining, and CI/CD for models and agent
Benefits:
- Diverse quantitative challenges across various domains
- The freedom to explore the rapidly evolving ML & AI landscapes from gradient boosting and deep learning to foundation models and agentic systems, while remaining grounded in rigorous experimentation and measurable business impact
- A collaborative team of data scientists, engineers, actuaries, underwriters, and product managers who enjoy solving difficult problems together
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