Principal ML & AI Scientist: High-Impact Insurance Risk

Accelerant; United Kingdom; On-site; GBP 80,000 - 120,000; Full time

Job summary

Accelerant is seeking a Data Scientist to develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations and claims. You will work with structured data, text and external sources, applying statistical modelling and modern AI techniques to solve challenging real-world problems.

The role centers on serious quantitative modelling, calibration and uncertainty, with a focus on production-ready solutions and measurable business impact.

Qualifications

• Strong foundation in statistics, probability, optimisation or applied mathematics.
• Sound modelling judgement for real-world deployment, not just validation.
• Solid programming skills.
• Willingness to work with LLMs and agentic AI in daily workflows.
• Clear communication to both technical and non-technical audiences.

Responsibilities

• Perform predictive modeling for pricing, underwriting, claims, catastrophe risk and portfolio management.
• Build classification, ranking, matching, recommendation, and anomaly detection systems.
• Extract information from documents, emails and forms using modern AI techniques.
• Create high-quality datasets through data enrichment and cleaning of unstructured data.
• Design AI systems to automate analytical workflows and measure business impact.
• Develop production feature pipelines, model inference services and evaluation frameworks.
• Collaborate with engineers, actuaries, underwriters, product managers and leaders to scale ML solutions.

Skills

Quantitative Foundation

Modelling Judgement

Programming Skills

LLMs & AI tooling

Communication

Tools

Python

TensorFlow/PyTorch

SQL

AWS/GCP

Job description

Accelerant is seeking a Data Scientist to develop machine learning and AI systems that improve decisions across pricing, underwriting, portfolio management, operations and claims. You will work with structured data, text and external sources, applying statistical modelling and modern AI techniques to solve challenging real-world problems.

The role centers on serious quantitative modelling, calibration and uncertainty, with a focus on production-ready solutions and measurable business impact.