Research Scientist – Causality Expert

Job description

Summary

We are looking for a motivated and high-achieving Research Scientist – Causality Expert based in London to join the team on our vision to optimise every business on the planet. This is a full-time placement with significant opportunities for personal development.


We offer an intellectually stimulating environment, work within an interdisciplinary team and an inclusive culture. We are a high-calibre, mission-driven team building a technology that improves our world.


Roles and Responsibilities

This is an exciting role for a smart, creative person to extend the cutting edge of Causality. The successful candidate will join the CLX research group focusing on Causality & Blue Sky Research.


The Company

causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect - a major step towards true AI. Its enterprise platform is used to transform leading businesses in Finance, IoT, Energy, Telecommunications and others.


Current machine learning approaches, including AutoML solutions, have severe limitations when applied to real world business problems and fail to unlock the true potential of AI for the enterprise. For instance, in the case of predictions, they severely overfit and do not adapt when the environment changes. causaLens’ Causal AI Platform goes beyond predictions, providing transparent causal insights and suggesting actions that directly improve business KPIs.


causaLens is run by scientists and engineers, the majority holding a PhD in a quantitative field. Contact us on info@causaLens.com or follow us on LinkedIn and Twitter.


We are committed to addressing the diversity problem in the tech industry, and that starts with making sure we have a team where everyone feels at home and can contribute as a peer.


causaLens in the News


Benefits

  • The opportunity to join a fast-growing, agile, and international team passionate about innovation and making a difference
  • Competitive remuneration
  • Share option scheme
  • Pension scheme
  • 32 days paid holiday allowance (incl. bank holidays)
  • Equipment you need to get the job done (MacBook Pro etc.)
  • Good work-life balance
  • Opportunities for continued learning and self-development, including courses, conferences and book budget
  • Flexible work-from-home and remote days
  • Cycle to work scheme
  • Weekly journal club and knowledge sharing presentations
  • Regular team outings, pizza Thursdays and annual company retreats
  • Fruits, snacks and soft drinks in the office
  • Amazing, smart, fun and inspiring colleagues, always there to support your ideas, growth and enthusiasm


Logistics

Our interview process consists of an intelligence test, interview and an on-site visit. We will do our best to transparently communicate the process with the successful candidates.

Job requirements

  • PhD in a quantitative field (machine learning / computer science / statistics / physics / mathematics, or comparable)
  • Research or Industrial experience in causality (e.g. causal inference and causal discovery methods) necessary
  • Creativity and ability to come up with ideas to tackle very hard problems and design/implement cutting edge solutions
  • Ability to translate advanced machine learning algorithms into code (Python preferred)
  • Familiarity with the general data science pipeline for time-series is a plus
  • Highly capable, self-motivated, collaborative and personable
  • Ability to demonstrate integrity and drive
  • Naturally curious and effective problem solver
  • An excellent written and verbal communicator with a high level of business acumen
  • Ability to effectively work independently in a fast-moving environment