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Staff Applied AI Scientist

Poshmark · US California (Redwood City) - Office

RemoteStaffScience and researchAll DepartmentsML & Data Engineering TeamPosted 3 weeks ago
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About this role

ABOUT POSHMARK Poshmark is the leading fashion marketplace where style comes alive through discovery, self-expression, and human connection. Powered by a vibrant community of 165 million members, Poshmark brings real people and taste to shopping through a social experience shaped by shared discovery. Buying and selling fashion feels simple, joyful, and personal, while every item tells its own story. Poshmark empowers sellers to grow meaningful businesses, keeps fashion in circulation longer, and gives shoppers access to unique and trusted finds, from everyday pieces to one-of-a-kind vintage and luxury. 💡ABOUT THE TEAM At Poshmark, we're passionate about harnessing the power of data to drive business impact. As an Applied AI Scientist, you'll tackle complex challenges in personalization, trust & safety, marketing optimization, and product experience. With over 130 million users generating billions of daily events, you'll be at the forefront of data science innovation, developing AI solutions that enhance our platform and delight our community. 🎯 ABOUT THE ROLE - Lead end-to-end data science initiatives, from ideation to deployment, delivering measurable business impact through projects such as feed personalization, product recommendation systems, computer vision and GenAI. - Collaborate cross-functionally with ML engineers, product managers, and business stakeholders, to design and deploy high-impact models. - Develop scalable solutions for key areas of product, marketing, operations, and community functions. - Own the entire ML development lifecycle: data exploration, model development, deployment, and performance optimization. - Explore and experiment with emerging AI trends, technologies, and methodologies to keep Poshmark at the cutting edge. 6-Month Accomplishments: - Develop comprehensive understanding of Poshmark's data platform and key datasets. - Gained insights into challenges related to data scale and noise, implementing strategies to effectively leverag

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