An experienced Applied Data Scientist with at least 2 years of commercial data science experience is needed to join our team at causaLens, a leading Causal AI company based in London, on a full-time basis.
Since its establishment in 2017, causaLens has launched decisionOS, the first enterprise decision-making platform powered by Causal AI, and open-sourced two internal tools. The company has raised $45 million in Series A funding and has been named a leading provider of Causal AI solutions by Gartner.
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.
This is an excellent opportunity to progress your data science career with a well-established company!
About Us
At causaLens we are building the world's most advanced Causal AI powered decision intelligence platform for Data Scientists. Our platform is trusted and used by data science teams in leading organisations and provides real value across various industries, and it's only the beginning.
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.
About the Role
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.
Key Responsibilities:
- 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
- Collaborating directly with business stakeholders to integrate domain knowledge into the modeling process, demonstrating how insights can enhance decision workflows
- Crafting long-term visions and plans, in collaboration with clients and causaLens stakeholders, to successfully implement causal models and insights into customers' strategies
- Work closely with the product and research teams to shape the development of our platform.
Required Experience:
- At least 2 years of commercial data science experience with time series or tabular use-cases, preferably using Python
- Strong academic record in a quantitative field (MEng, MSci, EngD or PhD)
- Excellent and proven communication and teamwork skills
- Previous experience in high growth technology companies or technical consultancy is a plus
- Previous experience in sales, pre-sales, and/or other technical evangelism is a plus
- Experience in supply chain, demand forecasting, retail/cpg, manufacturing, marketing, financial services, or public sector is a plus
Benefits:
- Good work-life balance
- Access to mental health support through Spill
- 25 days of paid holiday, plus bank holidays
- Share options
- Pension scheme
- Happy hours and team outings
- Referral bonus program
- Cycle to work scheme
- Friendly tech purchases
- Office snacks and drinks
Sounds interesting? Click the APPLY button to send your CV for immediate consideration.
Candidates with previous experience or job titles, including; Data Analyst, Machine Learning Engineer, Data Engineer, Research Data Scientist, Business Intelligence Analyst, AI Specialist, Quantitative Analyst, and Predictive Analytics Specialist may also be considered for this role.