Research Scientist

2 weeks ago


London, United Kingdom causaLens Full time

causaLens is the pioneer of Causal AI — a giant leap in machine intelligence.
Everything we do is at the forefront of technological advancements, and we are always on the lookout for people to join us whose skills and passion tower above the rest.
Open sourced two of our internal tools and packages to support the open-source community, see Dara and Causal Graphs .
At causaLens we are building the world's most advanced Causal AI powered decision intelligence platform for Data Scientists. The platform leverages state of the art Causal AI algorithms and models to empower data scientists and decision-makers to go beyond correlation-based predictions and have a real impact on the most important decisions for the business. Our platform is trusted and used by data science teams in leading organizations and provides real value across a wide variety of industries, and it's only the beginning.
A world in which humans leverage trustworthy AI to solve the greatest challenges in the economy, society and healthcare.
Head to our website homepage and watch the ‘Why Causal AI’ video to learn more.
Join our team as a Research Scientist and immerse yourself in the forefront of causal AI research.
As a key member of our dynamic team, you'll drive cutting-edge projects, develop core causal AI packages, and collaborate with expert colleagues to shape the future of the field. Enjoy autonomy over your work and make impactful contributions to groundbreaking research. If you're passionate about innovation, eager to push the boundaries of AI, and thrive in a collaborative environment and more importantly enjoy coding, we invite you to join us on this exciting journey towards shaping the future of AI.
Apply now and be part of a team where your voice is heard, your ideas valued, and your expertise celebrated, making a lasting impact on the field.

Cutting-Edge Algorithm Development: Join a team focused on advancing causal AI through algorithmic innovation and software package development.
Hands-On Coding: Expect to spend the majority of your time writing and optimising algorithms, and contributing to in-house or open-source code repositories.
Collaborative Innovation: Work closely with a skilled team to design and implement innovative solutions, with opportunities to shape the direction of our research roadmap.
Drive operational excellence by ensuring the smooth execution of research projects. Educational Background: PhD in Computer Science, Mathematics, Physics, or related field, with a strong academic track record. Outstanding motivated candidates with very strong coding skills without a PhD will be considered.
Research Experience: Demonstrated experience in conducting research, ideally with at least one significant publication in a reputable venue.
Work Experience: Research or Industrial experience in Machine Learning, Deep Learning or Reinforcement Learning is necessary
Technical Proficiency: Proficiency in coding, showcasing your ability to translate research ideas into practical implementations. Ability to translate advanced machine learning algorithms into code with Python
causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect — a major step towards true artificial intelligence. Our enterprise platform goes beyond predictions and provides causal insights and suggested actions that directly improve business outcomes for leading businesses in asset management, banking, insurance, logistics, retail, utilities, energy, telecommunications, and many others.
We offer a hybrid working setup and are dedicated to building an inclusive culture where diverse people and perspectives are welcomed. Beyond the core benefits like competitive remuneration, pension scheme, paid holiday, and a good work-life balance, we offer the following:
~ Access to mental health support through Spill
~ Competitive salary
~25 days of paid holiday, plus bank holidays
~ Pension scheme
~ Referral bonus program
~ Cycle to work scheme
~ Office snacks and drinks

Our interview process consists of a few screening interviews and a "Day 0" which is spent with the team (in-office).


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