Head of Data Strategy & Transformation

Munich Re
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Analytics Engineer

Data Engineer

Head, Medical Affairs Statistical Science

Chief Architect - Skynet Ground Systems

Programmatic Account Director

Head of Data - Leeds - Hybrid Remote - £110k - £140k

MRSG is part of the Munich RE group. We are known for our dedication to excellence and quality of service provision across both primary and reinsurance.

As well as operating in the Lloyd’s of London market, we conduct our business via a global network of service companies, with offices located in the UK, Ireland, the United States, Asia, including Singapore, Labuan and Dubai.

Our group of companies provide solutions covering Casualty, Marine & Cargo, Aerospace, Cyber, Political Violence, Trade Credit and Property, Contingency and Yacht. Our purpose is to inspire our clients and people with the confidence and freedom to explore, create and build – to enable people and businesses to thrive.

Head of Data Strategy & Transformation

We are looking to employ a Head of Data Strategy & Transformation to be based in London and to work with us on a Full Time basis.

If you’d like to work within an energetic, fun and collaborative team atmosphere where you can make a difference, we’d love to hear from you.

The Role

We are seeking an experienced and forward-thinking individual to join Munich Re Speciality Group as the Head of Data Strategy & Transformation within the Central Data Office, reporting to the Head of Data & Digital.

As the leader of our data strategy & transformation team, you will be responsible for developing and implementing a comprehensive data strategy that aligns with our business objectives, drives innovation, and leverages the power of data as an asset to steer business decisions.

You will collaborate with cross-functional teams to identify data-driven opportunities, establish an operating framework with business functions, and champion a data-driven culture. This role requires a deep understanding of the insurance industry, or financial services and large-scale transformation initiatives.

What we are looking for

As the Head of Data Strategy & Transformation for MRSG, you will play a critical role in shaping our organisation's data-driven future and driving innovation across Group’s Underwriting, Claims, Risk and Finance communities. Responsibilities include:

  1. Develop and execute a data strategy & vision that supports the overall business goals and objectives of Munich Re Speciality Group.
  2. Develop & lead a team of data SMEs, providing guidance, mentorship, and fostering a culture of data-driven decision-making and innovation.
  3. Collaborate with key stakeholders, including senior management, underwriters, actuaries, claims specialists, and IT teams, to identify strategic opportunities for leveraging data to drive business growth and operational efficiency.
  4. Establish a framework for the development of data products. Collaborate with data scientists, owners and engineers to define technical requirements and ensure successful implementation and delivery.
  5. Drive the product development process & lifecycle, from ideation and launch, ensuring timely delivery and high-quality discoverable data products to gain rapid ‘time to insight’ of information and content.
  6. Identify and prioritize data initiatives and projects based on their potential business impact, feasibility, and alignment with the data strategy.
  7. Drive the adoption of advanced data analytics and data science techniques to extract actionable insights from large and complex insurance datasets.
  8. Foster a culture of data literacy and data-driven decision-making across the organisation, providing training and resources to enhance data literacy and analytics capabilities.
  9. Collaborate with IT teams to assess and implement the necessary infrastructure, tools, and technologies to support the data strategy.
  10. Stay abreast of emerging trends, technologies, and best practices in data strategy, analytics, and the insurance industry, and assess their potential impact on business operations.
  11. Monitor and evaluate the performance of data initiatives, track key metrics, and provide regular reports to senior management on the effectiveness and ROI of data strategy efforts.
  12. Demonstrable experience in leading and managing matrix teams, fostering a collaborative and results-driven work environment.

Key Skills & Experience

  1. Proven experience (8+ years) in data strategy, data management, or a related field, preferably in the insurance industry. Experience in a leadership role is highly desirable.
  2. Strong understanding of the insurance industry, including underwriting, claims management, actuarial science, risk assessment, and pricing.
  3. Deep knowledge of data strategy frameworks, data governance, and regulatory requirements related to data privacy and security in the insurance industry.
  4. Familiarity with advanced data analytics techniques, statistical modelling, machine learning algorithms, and data visualization tools.
  5. Strong leadership and team management skills, with the ability to inspire and guide a team of data strategy professionals.
  6. Excellent analytical and problem-solving skills, with the ability to translate complex data into strategic insights and recommendations.
  7. Proficiency in understanding programming languages such as Python, R, or SQL, and experience using data manipulation and analysis tools like Qlik Sense, Power BI and Tableau.
  8. Exceptional communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at all levels of the organization.
  9. Strong business acumen and the ability to align data strategy initiatives with the strategic goals of the organisation.
  10. Office 365 (MS teams, MS Power Point, MS Excel, MS Word, SharePoint, Yammer)
  11. Business Intelligence Applications (PowerBI, QLIK Sense, Tableau, or similar etc.)
  12. SQL and SQL server reporting services (SSRS)

From a leadership perspective:

  1. Python, R (or other languages).
  2. Master Data Management Solutions (Informatica, Semarchy, IBM, or similar etc.)
  3. Meta Data Management Solutions (Colibra, Alex Solutions, or similar etc.)

Qualifications

  1. Bachelor's degree in a relevant field such as computer science, information management, or a related business discipline. Certifications e.g., Certified Data Management Professional is desirable.

Your career with us

At MRSG, you’ll find the flexibility, development and support you need to excel your career combined with a competitive salary and a benefits package that promotes wellbeing and work-life balance, on top of the standard features that include a non-contributory pension, private medical care, life assurance and more!

Diversity & Inclusion

Creating an inclusive environment is a crucial part of the Munich Re culture, and we are committed to our Diversity & Inclusion Policy. We also seek to provide a fair and supportive work environment which provides learning and development opportunities for all.

Working together, we are an employer of choice by building the workforce for today and the future.We make it happen. Together.

#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.

Careers Jobs

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Careers

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.

Jobs

Common Pitfalls Machine Learning Job Seekers Face and How to Avoid Them

Machine learning has emerged as one of the most sought-after fields in technology, with companies across industries—from retail and healthcare to finance and manufacturing—embracing data-driven solutions at an unprecedented pace. In the UK, the demand for skilled ML professionals continues to soar, and opportunities in this domain are abundant. Yet, amid this growing market, competition for machine learning jobs can be fierce. Prospective employers set a high bar: they seek candidates with not just theoretical understanding, but also strong practical skills, business sense, and an aptitude for effective communication. Whether you’re a recent graduate, a data scientist transitioning into machine learning, or a seasoned developer pivoting your career, it’s essential to avoid common mistakes that may hinder your prospects. This blog post explores the pitfalls frequently encountered by machine learning job seekers, and offers actionable guidance on how to steer clear of them. If you’re looking for roles in this thriving sector, don’t forget to check out Machine Learning Jobs for the latest vacancies across the UK. In this article, we’ll break down these pitfalls to help you refine your approach in applications, interviews, and career development. By taking on board these insights, you can significantly enhance your employability, stand out from the competition, and secure a rewarding position in the world of machine learning.

🍪 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.