Snabbfakta
-
- London
Ansök senast: 2024-11-30
Data Scientist (PhD)
causaLens is the pioneer of Causal AI — a giant leap in machine intelligence.We are on a mission to build truly intelligent machines, machines that truly understand cause and effect— it’s hard but super fun! 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.
Launched decisionOS, the first and only enterprise decision making platform powered by Causal AI -
hereOpen sourced two of our internal tools and packages to support the open-source community, see
Dara
and
Causal Graphs
.Been named a leading provider of Causal AI solutions by Gartner -
hereAt 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.
We are looking for a Data Scientist based in London to join us in spreading our Causal AI technology to every business on the planet. This is a full-time placement with significant opportunities for personal development. The Applied Data Scientist will develop causal-AI-driven models and decision applications using our technology to solve the most high-impact challenges in industries like retail, marketing, supply chain, manufacturing and finance.
As a Data Scientist at causaLens, you will play a pivotal role in advancing our Causal AI technology. This position demands a strong foundation in data science, particularly with time series or tabular use-cases, preferably using Python. Using our causal AI framework to build causal models and decision applications, using our proprietary causal discovery, modelling, and decision intelligence architectures on client-supplied data sets and use cases.Crafting long-term visions and plans, in collaboration with clients and causaLens stakeholders, to successfully implement causal models and insights into customers' strategies.At least 2 years of commercial data science experience with time series or tabular use-cases, preferably using PythonStrong academic record in a quantitative field (MEng, MSci, EngD or PhD)Previous experience in high growth technology companies or technical consultancy is a plusExperience in supply chain, demand forecasting, retail/cpg, manufacturing, marketing, financial services, or public sector is a plus
Current machine learning approaches have severe limitations when applied to real-world business problems and fail to unlock the true potential of AI for the enterprise. 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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