Lead Reserving Manager (Insurtech)

Arthur Recruitment
London
3 months ago
Applications closed

Related Jobs

View all jobs

General Manager

Engineering Manager - Autonomous Driving Forensics

Staff Data Engineer and Team Lead

Principal Hardware Cybersecurity Architect

UK 2025 Data Engineer Internship

Principal Scientist

I am working with agrowing Insurtechseeking theirfirst ever Actuaryto build out the reserving function. This will be a varied role,working closely with the Portfolio, U/W and Pricing team. My client will ideally be looking for someone with strong reserving experience with some experience in Pricing.



Responsibilities:


  • Build reserving models, processes, and documentation from the ground up
  • Deliver timely, data-driven insights and reports to internal stakeholders, ensuring clear communication of reserving trends and results
  • Work closely with pricing, underwriting, claims, and finance teams for a comprehensive view of portfolio performance
  • Continuously enhance reserving methodologies by integrating emerging best practices and addressing regulatory updates


Requirements:


  • In-depth knowledge of reserving methodologies within general insurance, supported by 4-8 years of relevant experience. Exposure to both insurer and MGA environments is highly desirable
  • Experience in managing relationships with senior stakeholders and external partners, demonstrating professionalism and influence
  • Proficient in actuarial software and programming tools such as ResQ, R, Python, or similar platforms
  • A strong understanding of pricing models, including GLMs and machine learning techniques, with the ability to critically evaluate their validity


Ideally London 1 day per week but there is some flex dependent on the candidate

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Careers

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Careers

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Careers

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.