Senior Quantitative Analystnew
Willis Towers Watson · Exchange / Data / Ratings
- All Quantitative Analyst jobs
- Quantitative & Modelling
- Exchanges, Data & Ratings
- Lisboa, Lisboa, Portugal
- Grade Analyst
- Experienced Hire · Full time
The Role
We are looking for a senior quantitative analyst to join our Strategic Investment Modelling Solutions (SIMS) team. The SIMS team develops, sells and supports a range of financial modelling software including economic scenario generator models used by our insurance and pension scheme clients. The role will involve a mixture of projects developing our range of models and client-facing activities. We are undertaking a significant program of development and restructuring of our products and processes - the successful candidate will play a key role in this.
You will receive on-the-job training and support to familiarize yourself with our business, models and systems. You will quickly be involved in regular calibration of our models, preparing client deliverables and responding to client queries. You will also collaborate in research projects, model testing and implementation, while developing subject-matter expertise in economic modelling.
This role offers flexibility in working pattern so please do discuss your requirements with us on application.
Responsibility
- Develop expertise in the stochastic mathematical models that underpin our economic scenario generators.
- Support the development and calibration of models, including the automation of key processes.
- Build knowledge of the full range of asset classes used by our clients and the associated modelling approaches.
- Prepare and deliver high-quality client outputs and documentation.
- Support clients by responding to queries, delivering training sessions, and conducting investigations, while collaborating with team members and providing guidance to junior colleagues.
You will be working in a small, collaborative team and you will be supported to apply your current skills and experience to our business. Depending on your previous experience we may offer support for study towards CFA or Actuarial qualifications, including a competitive package covering course materials and study days.
Bachelor's or Master's degree in Mathematics, Actuarial Science, Statistics, Economics, Finance, Engineering, or a related quantitative discipline.
Experience in quantitative modelling, with exposure to stochastic economic modelling considered a strong advantage, particularly within insurance capital modelling frameworks.
Working knowledge of programming languages such as Python and C#, with a strong interest in further developing technical and coding capabilities.
Strong analytical and problem-solving skills, with the ability to investigate complex modelling issues and communicate findings clearly to both technical and non-technical audiences.
Effective presentation and stakeholder communication skills, with the ability to explain complex concepts in a concise and engaging manner.
Ability to work independently and collaboratively in a hybrid working environment.
Strong project management and prioritisation skills, with the ability to manage multiple assignments concurrently and adapt to changing business needs.
Excellent organisational skills, attention to detail, and commitment to producing high-quality work.
What can we offer you?
At WTW, you’ll join a motivating and dynamic team in an exciting, fast-growing European capital. Here’s what we offer:
- Hybrid & flexible working – Choose between working from home or the office with flexible hours.
Competitive salary – Attractive, performance-based remuneration.
Global exposure – Work with international clients in a world-renowned consultancy.
Career growth – Fast-track your development with hands-on experience, on-the-job learning, and specialized training. - Collaborative culture – Work in a collegial, dynamic, and supportive team environment.
- Team & corporate events – Celebrate successes and foster a strong community.
We are committed to equal employment opportunities at Willis Towers Watson.
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