Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Principal Algorithmic Pricing Actuary – 27655

Emerald Group Ltd
London
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist: Recommender & Personalization Lead

Principal Data Scientist

Machine Learning Engineer (Databricks)

Principal AI Research Scientist – Natural Language Processing

Principal Data Scientist

Principal Data Scientist

Overview

Key Responsibilities: 

End-to-end ownership of companies digital pricing and underwriting governance capabilities Optimisation of model infrastructure, to develop, deploy, monitor and manage models at scale Development of automated validation and stress testing capabilities for companies pricing models Engage with other functions (e.g. Portfolio Underwriting, Product Engineering, Data Science) Input into validation of Machine Learning model development and other risk assessment considerations

Qualifications required:

Senior Qualified actuary (with significant post-qualification experience) or equivalent Qualified-by-Experience

Experience required:

Deep commercial general insurance Lloyd’s/company market experience Highly numerate and analytical Experience with predictive modelling approaches and good software development practices Experience working on data and modelling processes to support digital underwriting and portfolio management activities (e.g. mix, aggregation, catastrophe modelling) Familiarity with Machine Learning product design

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.