Staff Applied AI Scientist

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.

Responsibilities

• 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.
• Succeeded in leading the development and deployment of models for resolving business use case(s), contributing to overall company growth and success.
• Mentor junior scientists/engineers, fostering a culture of continuous learning and development within the team.

Requirements

• 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).