Principal Data Scientist – Machine Learning, AI

Accelerant

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.

Posted 7/21/2026full-timeRemote • 🇬🇧 United KingdomLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing machine learning and AI systems, with a strong quantitative foundation in statistics and applied mathematics. Proficient in ML engineering, model governance, and cloud technologies, particularly in the insurance domain.

Highest-signal resume keywords

Machine Learning DevelopmentStatistical ModelingLLM-Powered ApplicationsCloud Technologies (AWS, Azure, GCP)MLOps Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

StatisticsProbabilityOptimizationApplied MathematicsPredictive ModelingClassificationAnomaly DetectionCausal InferenceSurvival AnalysisHierarchical Models

Soft Skills

Clear CommunicationSound Modelling Judgement

Tools & Technologies

AWSAzureGCPContainersOrchestrationAPIsData Pipelines

Certifications & Qualifications

Actuarial Qualifications

Industry Keywords

Insurance Domain KnowledgePricingUnderwritingClaimsModel Governance

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud Platform

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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