Junior Quantitative Analyst

A Junior Quantitative Analyst role focused on using stochastic models and statistical analysis to assess financial risks and support pension fund resilience and decision-making.

The vacancy

At the Pension Protection Fund (PPF), we provide security in retirement for our members and millions of people throughout the UK who belong to defined benefit (DB) pension schemes. Through the PPF and Financial Assistance Scheme (FAS), we compensate members for their pensions after the employer funding their pension becomes insolvent. In total, we look after over 400,000 members across the PPF and FAS.

With over £30 billion in assets under management, our investments support long-term economic growth across the UK. Our award-winning team of around 450 professionals has earned the IPE UK Pension Fund of the Year title three times in seven years. We work closely with government and industry partners to improve outcomes for scheme members, employers, and the broader pension system—playing a vital role in strengthening retirement security nationwide.

We’re looking for a Junior Quantitative Analyst to join our Actuarial Risk & Modelling team, a core function responsible for providing robust quantitative analysis to support monitoring of the Pension Protection Fund’s financial resilience and the assessment of risks to our long‑term funding objective.

The team delivers its analysis through two complex stochastic models: the Long‑Term Risk Model (LTRM) and the Economic Scenario Model (ESM). As a Junior Quantitative Analyst, you will play an important role in supporting the team’s regular reporting and analysis cycle, alongside ad‑hoc modelling and investigative work.

You will develop a strong understanding of how the LTRM and ESM operate, how they are applied in practice, and how their outputs are used to inform key risk and funding assessments. The role also involves supporting the integrity and robustness of the end‑to‑end modelling and analysis process, including contributing to the accuracy, consistency and reliability of model inputs, processes and outputs. A key part of the role will be clearly communicating results and insights to colleagues with varying levels of technical knowledge.

Our ideal applicant will be educated to graduate level, or have equivalent experience, in a scientific or quantitative discipline, with a strong grounding in statistics and probability. You will have relevant experience of stochastic modelling gained within a financial institution, consultancy firm or academic environment.

The role requires strong numerical and analytical skills, alongside the ability to communicate clearly, both verbally and in writing, explaining complex model outcomes and methodologies to non‑technical audiences. Confidence in using Microsoft Office applications, particularly Excel, is essential, as is the ability to plan work effectively, manage competing priorities and meet deadlines.