Applied Data Scientist
1 week ago
causaLens is a pioneering company in the field of Causal AI, a new category of intelligent machines that understand cause and effect. Our enterprise platform goes beyond predictions and provides causal insights and suggested actions that directly improve business outcomes for leading businesses in various industries.
The RoleWe are seeking a highly skilled Data Scientist to join our team in London. As an Applied Data Scientist, you will play a pivotal role in advancing our Causal AI technology. Your primary responsibility will be to develop causal-AI-driven models and decision applications using our technology to solve high-impact challenges in industries such as retail, marketing, supply chain, manufacturing, and finance.
Key Responsibilities- Develop causal models and decision applications using our causal AI framework, leveraging our proprietary causal discovery, modeling, and decision intelligence architectures on client-supplied data sets and use cases.
- Collaborate directly with business stakeholders to integrate domain knowledge into the modeling process, demonstrating how insights can enhance decision workflows.
- Craft 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.
- 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.
- Competitive salary.
- 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.
Our interview process consists of a few screening interviews and a 'Day 0' which is spent with the team. We will always be as transparent as possible so please don't hesitate to reach out if you have any questions.
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