Who We Are 

Oliver Wyman is a business of Marsh (NYSE: MRSH), a global leader in risk, reinsurance and capital, people and investments, and management consulting, advising clients in 130 countries. With annual revenue of over $24 billion and more than 90,000 colleagues, Marsh helps build the confidence to thrive through the power of perspective.

Marsh is committed to creating a diverse, inclusive and flexible work environment. We aim to attract and retain the best people and embrace diversity of age, background, disability, ethnic origin, family duties, gender orientation or expression, marital status, nationality, parental status, personal or social status, political affiliation, race, religion and beliefs, sex/gender, sexual orientation or expression, skin color, or any other characteristic protected by applicable law.

Marsh is committed to hybrid work, which includes the flexibility of working remotely and the collaboration, connections and professional development benefits of working together in the office. All Marsh colleagues are expected to be in their local office or working onsite with clients at least three days per week. Office-based teams will identify at least one “anchor day” per week on which their full team will be together in person.

What You Will Do 

As a DNA Research Fellow, you will work with colleagues at all levels from your first day solving some of the world’s biggest challenges. This will include:  

• Designing solutions for complex problems using the latest research in data science, advanced analytics and AI 
• Implementing prototypes and measuring their effectiveness 
• Engaging in discussions with colleagues to share ideas and show results 

Your contributions will be crucial for creating cutting-edge products and carrying out advanced research and development within Oliver Wyman. 

Notable DNA projects include

• Developing an NLP-based machine learning toolkit to predict credit rating downgrades using news articles
• Exploring novel GenAI architectures and validation methods for customer-facing tools in the financial sector 
• Modelling portfolio decarbonisation strategies leveraging advanced analytics techniques for large European banks 
• Identifying supply chain risks by combining multiple live data feeds, and developing and applying a bespoke AI based risk methodology

Who Can Apply? 

• PhD students or early-career researchers in a quantitative discipline such as Mathematics, Statistics, Physics, Computer Science or, Engineering with capacity for at least a three-month fellowship. 
• Data-driven mindset and the ability to clearly explain complex ideas to colleagues 
• Familiarity with analytical methods in Python, R, or equivalent tools 
• Strong commitment to teamwork and resilience in a creative and constantly changing environment 
• Excellent command of the English language (verbal and written) 
• You are aged 18 or over at the start of the placement 
• You have the right to work in the UK 
• Interest or experience in financial services and risk management is a plus 
• Applicants with access to funding to support the fellowship will be preferred 

Why Join us? ​ 

• Expect a steep learning curve, as no two weeks are ever the same: 
• You’ll have access to the latest technologies and constantly expand your toolkit with each new endeavour 
• You’ll be working on cutting-edge research applying the latest ideas in AI, data and analytics to real-world problems 
• You’ll be part of a collaborative team, supported by experienced mentors and colleagues throughout your fellowship 
• You’ll have a dedicated buddy and a talent manager to help you navigate your professional path during your placement 
• We care deeply about sustainable work-life quality, providing you with the flexibility to balance your work with your personal life. 

If you’re excited by challenges and eager to make a meaningful impact, we look forward to welcoming you aboard! 

How to Apply?  

Please submit your CV, cover letter and transcripts. As part of your cover letter, could you please indicate if you have funding available to support this PhD placement? If so, kindly specify the amount of funding you can provide and the source(s) of this funding  

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