Quince
High Quality Products. Radically Lower Prices.
ecommerce • apparel • accessories • homegoods • luxury
11 - 50
Principal Data Scientist
August 18
🏡 Remote – Anywhere in California
Quince
High Quality Products. Radically Lower Prices.
ecommerce • apparel • accessories • homegoods • luxury
11 - 50
Description
• Architect and own data science solutions: Develop and maintain predictive models to address specific business problems • Collaborate across functions: Work closely with product, business, and engineering partners to initiate, develop, and deploy solutions with cross-functional support • Develop impactful models: Build models that support real-time or batch decisions across various business areas • Communicate insights: Present outcomes and insights to business stakeholders and leadership • Enhance data infrastructure: Identify gaps in existing data, create data product specifications, and collaborate with Engineering teams to implement enhanced data tracking • Promote best practices: Partner with Analytics team members and other functions to share insights and best practices, ensuring consistency in data-driven decision-making across the organization • Drive automation: Continuously strive for automated and production-ready solutions to improve efficiency and scalability • Design, implement, and optimize machine learning models • Analyze large datasets • Collaborate with cross-functional teams to integrate data science solutions into production, ensuring scalability and performance • Stay updated on the latest advancements in AI, deep learning, and machine learning algorithms to continuously improve existing systems
Requirements
• MS or PhD in statistics, mathematics, analytics, computer science or another quantitative field • 5+ years of experience in data science or applied science roles • Extensive experience in machine learning and deep learning, particularly within e-commerce • Proficiency in Python and SQL • Hands-on experience in ML and deep learning algorithms • Must have experience managing the entire data science lifecycle, including problem definition, data collection, preprocessing, model development, deployment, and ongoing monitoring and maintenance • Capable of delivering comprehensive solutions that address business challenges from start to finish • Building ML product into production; end-to-end
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