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

Apply Now

Data Science Analyst Insurance

Broad Street
4 weeks ago
Applications closed

Related Jobs

View all jobs

Data Science Analyst, General Insurance Risk Specialists | London, UK

Data Science Analyst, General Insurance Risk Specialists

Data Analyst / London

Senior Data Analyst

Analyst I/ II, Data Science

Safety Data Analyst

Our client, a well-established and actively expanding Lloyd's Syndicate Insurance firm, is seeking a Data Analyst with an interest in Data Science to join an expanding team in an expanding business that regard Data Science and Machine Learning as paramount.

The ideal candidate will have proven experience with the modelling of Data, strong interest or experience with Data Science and/or Machine Learning and be proficient with SQL, Python and R from a technical perspective.

THE ROLE:

  • The person in this role will play a key part in implementing the business and data modelling strategies to help achieve the company’s financial goals.

  • You will assist in developing data models and generating valuable insights to support the management of schemes and brokers across various products, while also contributing to pricing development and the pricing cycle.

  • The ideal candidate will bring expertise in advanced modeling and data science techniques, applying them as needed to meet specific project requirements.

  • To succeed in this role, strong collaboration with different business functions is essential to ensure the company’s resources are used effectively to achieve financial targets.

    RESPONSIBILITIES:

  • Apply advanced actuarial and data science methods to develop new pricing models and improve existing ones.

  • Collaborate with different business areas to ensure pricing changes align with product goals and overall objectives.

  • Analyze data to track and evaluate product performance.

  • Ensure timely delivery of projects by completing tasks within set deadlines and maintaining high-quality standards.

  • Work with management to align activities with the company’s strategy and broader business goals.

  • Contribute to the development of management information (MI) and reporting processes.

  • Assist in creating a robust change control process, supporting the deployment team in designing and executing effective change testing to enhance accuracy and manage risk.

  • Support the design, upkeep, and improvement of databases to boost data quality and analytical value, including researching and integrating new data sources.

  • Identify data integrity issues and escalate them through appropriate channels.

  • Continuously develop skills through on-the-job learning, industry events, online courses, and other external learning opportunities. Stay updated on the latest trends in data science and pricing methodologies within and beyond the insurance sector.

  • Ensure compliance with legal and regulatory requirements, as well as the company’s pricing governance framework.

    SKILLS / EXPERIENCE REQUIRED:

  • Proficient in data manipulation and statistical tools such as R, Python, SAS, EMBlem, RADAR, Excel/VBA, and SQL Server.

  • Skilled in analysing data to support effective strategies for various propositions.

  • Experienced in managing complex datasets and applying structured analytical methods.

  • Strong commercial awareness, using business insights to drive profitability.

  • Contributes to overseeing the financial performance of a wide range of products in a competitive and dynamic market.

  • Capable of creating clear, insightful visualizations of large and complex datasets using tools like Power BI.

  • A logical thinker, able to evaluate and develop multiple problem-solving approaches.

  • Innovative and creative in reviewing and enhancing processes

  • Strong communication skills, including effective presentation abilities

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.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.

Pre-Employment Checks for Machine Learning Jobs: DBS, References & Right-to-Work and more Explained

Pre-employment screening in machine learning reflects the discipline's unique position at the intersection of artificial intelligence research, algorithmic decision-making, and transformative business automation. Machine learning professionals often have privileged access to proprietary datasets, cutting-edge algorithms, and strategic AI systems that form the foundation of organizational competitive advantage and automated decision-making capabilities. The machine learning industry operates within complex regulatory frameworks spanning AI governance directives, algorithmic accountability requirements, and emerging ML ethics regulations. Machine learning specialists must demonstrate not only technical competence in model development and deployment but also deep understanding of algorithmic fairness, AI safety principles, and the societal implications of automated decision-making at scale. Modern machine learning roles frequently involve developing systems that impact hiring decisions, financial services, healthcare diagnostics, and autonomous operations across multiple regulatory jurisdictions and ethical frameworks simultaneously. The combination of algorithmic influence, predictive capabilities, and automated decision-making authority makes thorough candidate verification essential for maintaining compliance, fairness, and public trust in AI-powered systems.