Senior Pricing Actuary - Commercial Reinsurance

Selby Jennings
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Actuarial Data Scientist

Data Science Analyst

Product Management Assistant

SENIOR DATA SCIENTIST - Computer Vision / Generative AI HYBRID

Senior Data Engineer - Remote Working

Senior Python Developer

Our client is a reputable US-based insurer with well-established roots in London, operating through a number of Lloyd's of London syndicates that offer tailored and efficient (re)insurance solutions across robust and varied business classes, ranging from broadly available to exclusive and niche products.

They have seen tremendous return from their investments into their technology, machine learning, and AI, improving the accuracy of their modelling, the quality of their market data and ability to process/analyse large data-sets, as well as their efficiency and timeliness in generating and adapting pricing strategies. This has allowed them to enhance their competitiveness, increase profitability, and accelerate the growth of their products, both new and existing, and the wider business, while maintaining their reputation for quality and a positive, people-driven culture.

They place great value on their people, aiming to attract and retain the best minds in the (re)insurance sector, affording their staff with flexibility and promoting a healthy work-life balance, and emphasising a long-term platform for technical and professional development, through consistent access to support, advanced trainings and continued learning.

They are actively seeking to grow their Commercial Reinsurance team in London, in line with the increasing demand they have seen for this business. As a Senior Pricing Actuary, you will report directly to their Head of Pricing, seeing the opportunity both to receive hands-on mentorship from the group's leadership and to provide this to their more-junior staff, in a positive working environment that promotes collaboration, innnovation, and inclusion.

Key Responsibilities:

  • Analyse and interpret appropriate risk-related data across a variety of commercial reinsurance products.
  • Generate and adapt pricing strategies for both new and existing products/premiums.
  • Collaborate with underwriting staff to assess risk and develop tailored pricing solutions.
  • Monitor market trends and competitor strategies to inform pricing decisions and adjustments.
  • Offer mentorship and training to junior actuaries and analysts.
  • Present and justify findings to provide higher-level recommendations to senior management and stakeholders.
  • Ensure compliance with regulatory requirements and industry standards.

Role Requirements:

  • Minimum of a Bachelor's in a Numerate/Quantitative field.
  • Fellow of the Institute and Faculty of Actuaries (FIA) or equivalent.
  • 5+ years of experience in pricing for GI reinsurance and/or commercial lines.
  • Proficient with relevant actuarial software, programming and data analysis tools.
  • Strong communication and presentation skills.
  • Prior leadership and team management experience considered as beneficial.

#J-18808-Ljbffr

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.

Job-Hunting During Economic Uncertainty: Machine Learning Edition

Machine learning (ML) has firmly established itself as a crucial part of modern technology, powering everything from personalised recommendations and fraud detection to advanced robotics and predictive maintenance. Both start-ups and multinational corporations depend on machine learning engineers and data experts to gain a competitive edge via data-driven insights and automation. However, even this high-demand sector can experience a downturn when broader economic forces—such as global recessions, wavering investor confidence, or unforeseen financial events—lead to more selective hiring, stricter budgets, and lengthier recruitment cycles. For ML professionals, the result can be fewer available positions, more rivals applying for each role, or narrower project scopes. Nevertheless, the paradox is that organisations still require skilled ML practitioners to optimise operations, explore new revenue channels, and cope with fast-changing market conditions. This guide aims to help you adjust your job-hunting tactics to these challenges, so you can still secure a fulfilling position despite uncertain economic headwinds. We will cover: How market volatility influences machine learning recruitment and your subsequent steps. Effective strategies to distinguish yourself when the field becomes more discerning. Ways to showcase your technical and interpersonal skills with tangible business impact. Methods for maintaining morale and momentum throughout potentially protracted hiring processes. How www.machinelearningjobs.co.uk can direct you towards the right opportunities in machine learning. By sharpening your professional profile, aligning your abilities with in-demand areas, and engaging with a focused ML community, you can position yourself for success—even in challenging financial conditions.

How to Achieve Work-Life Balance in Machine Learning Jobs: Realistic Strategies and Mental Health Tips

Machine Learning (ML) has become a cornerstone of modern innovation, powering everything from personalised recommendation engines and chatbots to autonomous vehicles and advanced data analytics. With numerous industries integrating ML into their core operations, the demand for skilled professionals—such as ML engineers, research scientists, and data strategists—continues to surge. High salaries, cutting-edge projects, and rapid professional growth attract talent in droves, creating a vibrant yet intensely competitive sector. But the dynamism of this field can cut both ways. Along with fulfilling opportunities comes the pressure of tight deadlines, complex problem-solving, continuous learning curves, and high-stakes project deliverables. It’s a setting where many professionals ask themselves, “Is true work-life balance even possible?” When new algorithms emerge daily and stakeholder expectations soar, the line between healthy dedication and perpetual overwork can become alarmingly thin. This comprehensive guide aims to shed light on how to achieve a healthy work-life balance in Machine Learning roles. We’ll discuss the distinctive pressures ML professionals face, realistic approaches to managing workloads, strategies for safeguarding mental health, and how boundary-setting can be the difference between sustained career growth and burnout. Whether you’re just getting started or have been at the forefront of ML for years, these insights will empower you to excel without sacrificing your well-being.

Transitioning from Academia to the Machine Learning Industry: How PhDs and Researchers Can Thrive in Commercial ML Settings

Machine learning (ML) has rapidly evolved from an academic discipline into a cornerstone of commercial innovation. From personalising online content to accelerating drug discovery, machine learning technologies permeate nearly every sector, creating exciting career avenues for talented researchers. If you’re a PhD or academic scientist thinking about leaping into this dynamic field, you’re not alone. Companies are eager to recruit professionals with a strong foundation in algorithms, statistical methods, and domain-specific knowledge to build the intelligent products of tomorrow. This article explores the essential steps academics can take to transition into industry roles in machine learning. We’ll discuss the differences between academic and commercial research, the skill sets most in demand, and how to optimise your CV and interview strategy. You’ll also find tips on networking, developing a commercial mindset, and navigating common challenges as you pivot your career from the halls of academia to the ML-driven tech sector.