Asset Modelling Analyst
The successful candidate will work on projects developing, maintaining and calibrating economic and asset models in our STAR ESG software for use by insurers, asset managers, sovereign wealth funds and pension funds to form strategic asset allocation and manage financial risk. In addition, you will deal with queries from clients and assist with client training. To excel in this role, you need to be comfortable with working on several projects simultaneously in a team environment, but also able to structure your work independently and deliver concise results.
There is a comprehensive study package for either the CFA or Actuarial exams. It is expected candidates will wish to further their studies in one of these relevant professional disciplines although other options may be considered on a case-by-case basis.
- Develop new functionality and improve efficiency for Willis Towers Watson's asset modelling software, including development of existing and new functionality in C#
- Provide support to users of our STAR ESG software
- Research, analyse and implement new asset classes in the model.
- Provide support and deliver specific clients' modelling projects which also includes data analysis and assumption proposal.
- Provide support on Willis Towers Watson STAR ESG model integration and development projects
- Preferably a degree in a quantitative subject (Mathematics, Computer Science, Engineering or Finance) with a minimum requirement of an A grade at A-level maths.
- Enquiring and analytically minded with a logical and thorough work ethic.
- Good Excel skills and familiarity with object-oriented programming languages (C# preferred) and Matlab.
- Good communication skills and can work with both quant analysts and client consultants efficiently.
- Team player comfortable in a professional services environment with the ability to debate effectively and subsequently influence internally & externally at all levels
- Previous experience in an economic or asset modelling role and/or CFA candidate would be an advantage.
Willis Towers Watson is a leading global advisory, broking and solutions company that helps clients around the world turn risk into a path for growth. With roots dating to 1828, Willis Towers Watson has 40,000 employees serving more than 140 countries. We design and deliver solutions that manage risk, optimize benefits, cultivate talent, and expand the power of capital to protect and strengthen institutions and individuals. Our unique perspective allows us to see the critical intersections between talent, assets and ideas - the dynamic formula that drives business performance. Together, we unlock potential. Learn more at willistowerswatson.com.
Our sophisticated approach to risk helps clients free up capital. We work in close concert with investors, reinsurers, and insurers to manage the equation between risk and return. Blending advanced analytics with deep institutional knowledge, we reveal new opportunities to maximize performance.
As companies struggle to improve business performance, they increasingly expect risk management to support the broader financial objectives. Willis Towers Watson understands the crucial link between risk and capital, whether you are an insurer concerned about capital management, a CFO focused on risk management or an investment committee seeking to balance risk and return. As a leading investment consultant, we help organizations manage investment complexity, establish risk tolerance and improve governance.
The economic and asset modelling team operates globally and provides investment analytics and consultancy to clients spanning the international insurance, pension, banking, endowment and private / sovereign wealth sectors. Our macroeconomic and asset models underlie risk and investment management advice provided to clients with over $4tn of asset exposure across a diverse range of products.
Willis Towers Watson is an equal opportunities employer and does not discriminate on any basis. We support flexible working and this role will be considered on a flexible basis.