Sr. Data Scientist
About Poshmark
Poshmark is a leading fashion resale marketplace powered by a vibrant, highly engaged community of buyers and sellers and real-time social experiences. Designed to make online selling fun, more social and easier than ever, Poshmark empowers its sellers to turn their closet into a thriving business and share their style with the world. Since its founding in 2011, Poshmark has grown its community to over 130 million users and generated over $10 billion in GMV, helping sellers realize billions in earnings, delighting buyers with deals and one-of-a-kind items, and building a more sustainable future for fashion. For more information, please visit , and for company news, visit newsroom.poshmark.com .
Poshmark Inc. is hiring for the following role in Redwood City, CA: Sr. Data Scientist to drive end-to-end data science initiatives, from ideation to deployment, delivering measurable business impact through projects such as feed personalization, product recommendation systems, and attribute extraction using computer vision.
Responsibilities
Collaborate with cross-functional teams, including software and machine learning engineers, and business stakeholders to design and deploy high-impact models;
Develop scalable data science solutions in a big data environment for product, marketing, operations, and community functions;
Assist with machine learning (ML) development lifecycle, including data exploration, model development, deployment and performance optimization;
Apply best practices for managing and maintaining machine learning models in big data production environments; and
Explore and experiment with emerging AI trends, technologies, and methodologies to keep Poshmark at the cutting edge. Telecommuting available. Multiple positions.
Qualifications
Master’s degree (or foreign equivalent) in Computer Science, Data Science, Mathematics, Statistics, or a related field;
Two (2) years of experience in the job offered or related occupation.
Requires two (2) years of experience in each of the following: Building scalable data science solutions in big data environment; Machine learning, including algorithms, CNNs, Transformers, and Vision Transformers; Large Language Models (LLMs), including RAG or PEFT; Programming languages: Python, SQL, and Spark (Scala or PyTorch); PyTorch, or TensorFlow; and Real-world application of Mathematical Foundations, including linear algebra, statistics, probability, calculus, and A/B testing concepts.
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