Applied Scientist, Modelling & Decision Science, Defence

London

Join our defence and national security business and you’ll be helping users plan and train to meet the most complex security threats in the world. Working alongside experts in AI and Machine Learning, computational modelling and distributed systems, you’ll be putting multiplayer technology to life-saving use.
We are an innovative team of applied scientists who use statistical techniques to help teams across the business. We provide the best customer experience by focusing on mitigating risk and providing support for solutions.
Our teams provide the scientific models that sit at the heart of our core product offering. This means building data pipelines to generate accurate representations of real-world environments as well as algorithms for simulating their dynamics. A major part of this work is to provide evidence to validate and verify how this was done (Explain and Review). Our goal is to develop a division-wide E&R framework which can be applied to any project. This will provide a decision-maker with information to help them understand how our modelling and simulation tools relate to reality.

Areas of Impact:

  • Working closely with applied scientists & model engineers on customer project teams who are building modelling & simulation tools; in particular advising on explainability and verification & validation of models.
  • Working closely with UX designers on customer project teams; in particular advising on how to present information on modelling & simulation tools to users with a range of technical knowledge, in a way that is comprehensible without being misleading.
  • Folding back learnings from work with project teams into a platform-wide ‘Explain & Review’ programme which will ultimately power all model explainability and validation for the Skyral platform.
  • Contributing to capturing metadata on our modelling and data assets to facilitate automated presentation of model functioning and reliability.
  • Working with the research team, to keep abreast of their work on validation and determine how it can be productionised.

We’d like to hear from you if you identify with the following:

  • You have experience with a range of modelling paradigms and techniques such as agent-based modelling, machine learning, optimisation or Bayesian statistics
  • Experience across the whole lifecycle of a model including requirements gathering, converting these into actionable tasks, model implementation, validation and deployment
  • Experience communicating complex concepts (e.g. scientific modelling) to a range of audiences both technical and non-technical
  • You have previous experience with validating models via a range of methods
  • Exposure to knowledge representation is a plus
  • You have a  good first degree in a STEM subject with a significant mathematical component. A PhD is not required but is an advantage
  • Programming skills in Python.
While we think the above experience could be important, we're keen to hear from people that believe they have valuable experience to bring to this role. If you identify with the team and mission, but not all of our requirements, then please still apply.

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Improbable is determined to foster an environment where people can do their best work and feel like they belong. We believe a healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success.
We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender identity, age disability, veteran status, genetic information, marital status or any other legally protected status.

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