Join us

We pride ourselves on our open and supportive culture, in which people can develop their cutting-edge technology and business skills. We offer a competitive salary plus bonus potential, pension contributions, a bike to work scheme, and private healthcare, all in a relaxed and flexible working environment at our offices in Hove.

We are always keen to hear from aspirational and motivated individuals who feel they have something to add to our team.

If that’s you, please email us.

 
We currently have the following opening:

Applied mathematician

We are looking to extend a skunk works team committed to exploring the possibilities of Machine Learning and Artificial Intelligence initially in the area of Digital Content Optimisation.

Position Summary
The objective of the applied mathematician role is to help a small team of mavericks invent transparent, fair, cost effective Artificial Intelligence for Digital Content Optimisation.

Duration
Six months at 37.5 hour/week, with possibility of full-time employment depending on success of the project.

About you
You are an independent thinker and embrace constraints. For you, simple wins every time. You see solutions, never problems. You bring imagination, attention to detail and reasoning from first principles. You’re proactive, responsible, accountable and play well with others. Willing to do things differently.

Responsibilities

• Finding, understanding and educating on relevant academic literature.
• Devising mathematically sound models and strategies for Digital Content Optimisation.
• Understanding, extending and improving our existing Python models.
• Prototyping new models and solutions mainly in Python.
• Devising strategies and metrics for testing and measuring performance of models and solutions.

Requirements

• Graduate degree (master, phd) in applied mathematics or an equally mathematically-focussed discipline. Alternatively, demonstrable commercial experience with strong evidence of original research and development.
• Experience understanding existing models and implementing new mathematical models and algorithms using Python.
• Some experience with Natural Language Processing (NLP) using Python libraries like Panda, Numpy, sklearn, NLTK and gensim would be a definite advantage.
• Strong foundation and deep interest in Bayesian / Probabilistic reasoning and models.
• Familiarity with optimisation techniques and solutions.
• Strong communication skills