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causaLens
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London, Greater London
Quantitative Analyst
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Job Type: Full-time |
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Contract Type: Permanent |
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Salary: 30000 - 70000 per annum |
Overview
Summary We are looking for a motivated and high-achieving Quantitative Analyst based in London to join the team working on our exciting Machine Learning product. 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. The Company vision global_economy.predict().optimize() causaLens is pioneering a completely new approach to time-series prediction. Its Enterprise Platform is used to transform and optimise businesses that need accurate and robust predictions – including significant businesses in finance, IoT, energy and telecoms. Almost all current machine learning approaches, including AutoML solutions, severely overfit on time-series problems and therefore fail to unlock the true potential of AI for the enterprise. causaLens was founded with the mission to devise Causal AI , which does not overfit, and so provides far more reliable and accurate predictions. The platform also includes capabilities such as autonomous data cleaning and searching, autonomous model discovery and end-to-end streaming productisation. causaLens is on a mission to build truly intelligent machines that go beyond current machine learning approaches - a curve-fitting exercise. Devising Causal AI has allowed us to teach machines cause and effect for the first time - a major step towards true AI. causaLens is run by scientists and engineers, the majority holding a PhD in a quantitative field. For more information visit www.causaLens.com or contact us on infocausaLens.com. Follow us on LinkedIn and Twitter . causaLens in the News Best Deeptech Company 2019 - Artificial Intelligence Awards ‘Meet causaLens, a Predictive AI For Hedge Funds, Banks, Tech Companies ’ – Yahoo Finance ‘The U.K.’s Most Exciting AI Startups Race To Scale’ - Forbes ‘Auto ML Platform Draws Interest from Discretionary Funds’ - machineByte ‘ AllianzGI Taps Virtual Data Scientists amid War for Talent’ - Financial Times ‘Machine Learning Companies to watch in Europe’ - Forbes ‘ Best Investment in Deeptech ’ award - UK Business Angels Association awards ‘100 Most Disruptive UK Companies’ - Hotwire ‘causaLens Appoints Hedge Fund Veteran and Data Leaders to Advisory Board’ - Newswire Roles and Responsibilities: This is an exciting role for a smart, creative person to build sophisticated predictive models on time-series data using the causaLens platform. 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. Requirements Core requirements: ● Experience building predictive models ● Highly capable, self-motivated, collaborative and personable ● Strong academic record ● Interest in advanced technologies such as machine learning ● 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 and remotely (from home or coworking space)