Medical Analytics & R&D Manager

• A leading global life reinsurer is looking to hire a Medical Analytics Manager into its R&D team, supporting the continued growth of its UK individual and enhanced annuities business.

  This is a particularly interesting opportunity for someone who can combine strong analytical capability with commercial judgement and stakeholder management. The role sits at the intersection of health, medical research, data analytics and insurance, using health evidence and modelling to help shape assumptions, pricing and underwriting decisions.

  The business is open to candidates from either an actuarial background or a health sciences / analytics background.

  The role
  

  You will work on health related analytical and research projects, helping to turn complex medical evidence, data and modelling outputs into practical conclusions that can be used by pricing, underwriting and other commercial stakeholders.

  You will:

  - Manage and deliver health related analytical projects from initial scoping through to communicating results

  - Review and synthesise medical research and other evidence to support assumption development

  - Analyse health and underwriting data and interpret existing statistical models

  - Translate quantitative and qualitative analysis into assumptions, adjustments and commercially useful recommendations

  - Work closely with pricing actuaries, underwriters, medical experts, data scientists and other R&D colleagues

  - Explain complex technical and medical findings clearly to both technical and non technical stakeholders

  - Support the development of assumptions for individual and enhanced annuity business

  - Contribute to wider Medical Analytics research and thought leadership

  This is not necessarily a role for someone whose main strength is building highly complex models from scratch. Of greater importance is the ability to understand modelling, apply judgement, interpret the results and explain what those results mean commercially.

There are two main backgrounds that could work particularly well.

You may be a qualified actuary with experience in areas such as:

- Life insurance or reinsurance pricing

- Underwriting

- Mortality or longevity

- Experience analysis

- Assumption setting or basis development

- Protection or annuities

- Health or biometric risk analytics

Approximately 3 to 6 years PQE is likely to be a sensible level, although there is flexibility for particularly relevant candidates.

Experience working with pricing or underwriting teams would be especially valuable.

Alternatively, you may come from outside insurance and hold an advanced qualification such as a Masters or PhD in Epidemiology, Biostatistics, Health Data Science, Health Economics, Medical Statistics or another closely related discipline.

Candidates from pharma, life sciences, health analytics, academia or medical research could all be considered.

For candidates coming from a more academic or research focused background, some evidence of commercial exposure is important. The business is particularly interested in people who can bridge the gap between academic problem solving and a commercial environment.

This could include experience:

- Working with business or commercial stakeholders

- Turning complex analysis into practical recommendations

- Presenting technical findings to non specialists

- Delivering work to commercial deadlines

- Understanding how analytical outputs influence business decisions

- Working within pharma, life sciences, consulting, health analytics or another commercially focused organisation

Relevant technical experience could include survival analysis, epidemiological or biostatistical modelling, mortality or morbidity analysis, disease progression modelling, causal inference, real world evidence, health economics or evidence synthesis.

Strong statistical programming capability is required, with R particularly relevant to the team's existing modelling environment. Strong Python experience may also be considered.

You do not need to be a specialist in every modelling technique. The ability to understand existing models, interrogate results and use them intelligently is more important than being a pure technical modelling specialist.

The strongest candidates will combine:

- Strong analytical and problem solving ability

- An understanding of health, mortality or medical evidence

- Commercial judgement

- Excellent communication skills

- Strong stakeholder and project management

- The ability to translate technical outputs into clear business decisions

The ideal candidate will be someone who can bridge the gap between technical or academic analysis and commercial decision making, working effectively with actuaries, underwriters, medical experts, data scientists and other stakeholders.

This is a London based role, so candidates should either already be within reasonable commuting distance or be willing to relocate to the London area.