Accelerant
50-200 employees
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About Accelerant
Accelerated
1 day ago
Principal Data Scientist – Machine Learning & AI
Full-time
Lead
Data Scientist
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Description
- Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers.
- Accelerant was founded in 2018 by a group of longtime insurance industry executives and technology experts who shared a vision of rebuilding the way risk is exchanged – so that it works better, for everyone.
- The Accelerant risk exchange does business across more than 20 different countries and 250 specialty products, and we are proud that our insurers have been awarded an AM Best A- (Excellent) rating.
- We're looking for a Data Scientist to develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations, and claims.
- You'll 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.
- The foundation of this role is serious quantitative modelling.
- We care about calibration, not just discrimination.
- We validate out of time and worry about leakage and drift.
- We quantify uncertainty and can tell you when a model should be trusted, when it shouldn't, and why.
- LLMs and agentic systems are a force multiplier on all of that and we measure those systems the way we'd measure any other model: on data they haven't seen, against a sensible baseline, with honest uncertainty around the result.
- You don't need an AI background to join us; you do need genuine enthusiasm for working this way.
- This is not a reporting or dashboard role.
- You'll work on ambiguous, high-impact problems where you'll be expected to identify the right approach, build production-ready solutions, and measure the business impact of your work.
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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