Staff Applied AI Scientist

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 objectives

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 success

Lead 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.

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.

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).