Senior Data Scientist II

Confidence can sometimes hold us back from applying for a job. Here’s a secret: theres no such thing as a "perfect" candidate. Poshmark is looking for exceptional people who want to make a positive impact through their work and help create an organization where everyone can thrive. So whatever background you bring with you, please apply if this role would make you excited to come to work every day.

Job Description

Senior Data Scientist II

Are you passionate about the power of data and excited to leverage cutting-edge AI/ML to drive business impact? At Poshmark, we tackle complex challenges in personalization, trust & safety, marketing optimization, product experience, and more.

Why Poshmark?

As a leader in Social Commerce, Poshmark offers an unparalleled opportunity to work with extensive multi-platform social and commerce data. With over 130 million users generating billions of daily events and petabytes of rapidly growing data, you’ll be at the forefront of data science innovation. If building impactful, data-driven AI solutions for millions excites you, this is your place.

What You’ll Do

• 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.
• Collaborate with cross-functional teams, including ML engineers, product managers, and business stakeholders, to design and deploy high-impact models.
• Develop scalable solutions for key areas like product, marketing, operations, and community functions.
• Own the entire ML Development lifecycle: data exploration, model development, deployment, and performance optimization.
• Apply best practices for managing and maintaining machine learning models in production environments.
• Explore and experiment with emerging AI trends, technologies, and methodologies to keep Poshmark at the cutting edge.
  
  

• Experience: 6-9 years of building scalable data science solutions in a big data environment. Experience with personalization algorithms, recommendation systems, or user behavior modeling is a big plus.
• Machine Learning Knowledge: Hands-on experience with key ML algorithms, including CNNs, Transformers, and Vision Transformers. Familiarity with Large Language Models (LLMs) and techniques like RAG or PEFT is a bonus.
• Technical Expertise: Proficiency in Python, SQL, and Spark (Scala or PySpark), with hands-on experience in deep learning frameworks like PyTorch or TensorFlow. Familiarity with ML engineering tools like Flask, Docker, and MLOps practices.
• Mathematical Foundations: Solid grasp of linear algebra, statistics, probability, calculus, and A/B testing concepts.
• Collaboration & Communication: Strong problem-solving skills and ability to communicate complex technical ideas to diverse audiences, including executives and engineers.