Bayesian Modeller

Sharnbrook, Bedfordshire, UK

Our thinking keeps Unilever in the lead as one of the largest and most successful FMCG companies. Its one of the reasons why, all over the globe, 150 million times a day, someone chooses one of our products to bring into their home.

As a Bayesian modeller with our Safety and Environmental Assurance Centre (SEAC), you will provide capability for assessing the risks that chemicals present in consumer products sold by Unilever across the globe. You will work in multidisciplinary teams alongside toxicologists, chemists and other modellers by applying modelling and data analysis techniques that directly contribute to toxicology risk assessments. You will lead the application of Bayesian analysis and modelling in the development of novel approaches to risk assessment and contribute to external acceptance of these approaches through active publication and external representation.

Responsibilities:

1. Identify risk assessment needs

• Work with toxicology modellers, risk assessors and chemists to identifying toxicology risk assessment needs in the context of Bayesian analysis and modelling

• Identify and apply new techniques and probabilistic modelling approaches

2. Build risk assessment capability

• Lead the application of Bayesian analysis and modelling in the development of novel approaches to risk assessment including

  • Bayesian networks & Probabilistic risk analysis

• Provide clear, consistent and transparent analysis and models for toxicological risk assessment

• Interact with other modellers across SEAC to promote best practices in modelling and communication

• Provide consultancy, advice and training on analysis and modelling

• Recommend data requirements for the development of new models

• Clear communication to non-specialist audiences

3. Build an external profile

• Actively present, publish and liaise with relevant external communities for probabilistic risk modelling

Experience:

• A PhD qualification (or equivalent experience) in Bayesian modelling and/or analysis a with a first degree in either Statistics, Mathematics, Physics, Engineering or Computer Science

• Experience of applying Bayesian techniques to solve real-world problems using experimental data (preferably biological)

• Computational experience of building models and performing analysis using WinBUGS, MATLAB, R, C++ or other relevant programming languages

• Postdoctoral or proven industry experience of developing Bayesian networks for decision making under uncertainty would be advantageous

Skills:

• Good communication and influencing skills

• Proven experience of working in teams

To find out more about this role and apply, please visit www.unilever.co.uk/careers