Principal Data Scientist – Machine Learning, AI

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Accelerant

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

☁️ SaaS

🤝 B2B

💰 $150M Private Equity Round - Accelerant on 2023-06

SaaS • B2B

Accelerant is a digital-first risk exchange and insurance platform that connects managing general agents (MGAs), underwriters, reinsurers, and institutional capital through a tech-powered, data-driven marketplace. The platform provides real-time analytics, underwriting tools, performance metrics, and operational support (actuarial, claims, regulatory) to streamline specialty insurance distribution and enable faster, more transparent capital deployment. Accelerant positions itself as a SaaS-style partner for specialty insurance firms, focused on improving efficiency, transparency, and profitable growth.

📋 Description

• 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