Staff Data Scientist

About PoshmarkPoshmark 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.At Poshmark, we're passionate about harnessing the power of data to drive business impact. As a Lead Data 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.Responsibilities: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.Must-Have Qualifications:5–8 years of experience building scalable data science solutions in a big data environment.Hands-on experience with key machine learning algorithms, including CNNs, Transformers, and Vision Transformers.Proficiency in Python, SQL, and Spark (Scala or PySpark), with experience in deep learning frameworks such as PyTorch or TensorFlow.Solid understanding of linear algebra, statistics, probability, calculus, and A/B testing concepts.Strong problem-solving skills and the ability to communicate complex technical ideas effectively to diverse audiences, including executives and engineers.Preferred Qualifications:Experience with personalization algorithms, recommendation systems, or user behavior modeling.Familiarity with Large Language Models (LLMs) and techniques such as Retrieval-Augmented Generation (RAG) or Parameter-Efficient Fine-Tuning (PEFT).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 leverage data in decision-making processes.Demonstrated proficiency in Poshmark's machine learning systems by applying advanced algorithms to address a business use case.Successfully prototype/enhance models aimed at improving key business metrics, contributing to data-driven solutions that support company objectives12+ Month Accomplishments:Collaborated with ML engineers and other data scientists to establish best practices for managing and maintaining machine learning models in production, enhancing system reliability.Successfully led the development and deployment of model for resolving business use case(s), contributing to overall company growth and successLead the adoption and application of cutting-edge AI trends and technologies, establishing Poshmark as a pioneer in data science.Mentor junior scientists/engineers, fostering a culture of continuous learning and development within the team.Compensation Range: $188,936 - $268,149LocationUS California (Redwood City) - OfficeEmployment TypeFull timeDepartmentAll DepartmentsResearch & DevelopmentTechnologyML & Data Engineering TeamCompensation$188,936 – $268,149The base pay range listed for this position may span multiple levels of experience, and final compensation is determined by a variety of job-related factors, including relevant experience, demonstrated expertise, and internal equity.The posted range applies to base salary only and does not include additional elements of total compensation, which may include equity, performance-based incentives, and a comprehensive benefits package.