Remote Lead Data Scientist

Cohere

San Francisco, CARemote OKFull-time$140k – $220k / year

About this role

Remote Lead Data Scientist at Cohere | RemoteFront Lead Data Scientist Apply Lead Data Scientist Remote Remote· about 1 month ago Straight from Cohere’s careers page. Apply on the company site — no recruiter, no middleman. Applycreate free resume! SaveCopy link Explore more remote Data Scientist jobs — salaries, top companies, and the latest openings.See all → Lead Data Scientist Department: Agentic Platform Location: United States, Canada Employment Type: FullTime Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us! Why this role? Were building the foundational infrastructure that will define how the world thinks about and deploys AI, and we want the sharpest, most curious people to help us do it. As a Lead Data Scientist on our Analytics and Data Insights team, youll tackle problems that dont have textbook answers yet; shaping go-to-market strategy for technology thats still being invented, designing the experiments that prove or kill our biggest bets, and helping enterprises understand what foundational AI actually means for their bottom line. Youll own the full analytical lifecycle, from framing the right questions and building the models, to le

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