Note: The job is a remote job and is open to candidates in USA. Accelerant is a data-driven risk exchange connecting underwriters of specialty insurance risk with risk capital providers. They are seeking a Principal Data Scientist to develop machine learning and AI systems that enhance decision-making processes across various sectors including pricing and underwriting.
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
- Predictive modeling for pricing, underwriting, claims, catastrophe risk, and portfolio management
- Classification, ranking, matching, recommendation, and anomaly detection systems that improve business decision-making
- Information extraction from documents, emails, forms, and other unstructured data using modern AI techniques
- Entity resolution, data enrichment, and building high-quality datasets from noisy or incomplete information
- Design AI systems that automate analytical and decision-making workflows end to end
- Build the measurement that tells us whether they genuinely outperform what they replace
- Building production feature pipelines, model inference services, and evaluation frameworks
- Collaborating with engineers, actuaries, underwriters, product managers, and business leaders to turn ambiguous questions into scalable machine learning solutions
Skills
- 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
- 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
Company Overview