Quince
San Francisco
51-200 employees
Quince brings luxury products like Mongolian Cashmere, Italian Leather, Turkish Cotton and Washable Silk to everyone at radically low prices.
Staff Data Scientist - Storefront
Staff Data Scientist - Storefront
OUR STORY
Quince was started to challenge the existing idea that nice things should cost a lot. Our mission was simple: create an item of equal or greater quality than the leading luxury brands and sell them at a much lower price.
OUR VALUES
EVERYONE SHOULD BE ABLE TO AFFORD NICE THINGS. Quality shouldn’t be a luxury. We’re proud of our mission to bring the world’s highest quality goods to people at affordable prices.
QUALITY IS MORE THAN MATERIALS. True quality is a combination of premium materials and high production standards.
WE FOCUS ON THE ESSENTIALS. From the perfect crewneck sweater to hotel quality sheets, we're all about high quality essentials that bring enjoyment to daily life.
WE’RE INNOVATING TO MAKE UNREAL PRICES A REALITY. Our uniquely developed factory-direct model lets us offer exceptionally high quality goods for much lower prices than our competitors.
ALWAYS A BETTER DEAL. We believe in real price transparency, for both our customers and factory partners. This way, everyone gets a better deal.
FAIR FACTORIES. We are committed to working with factories that meet the global standards for workplace safety and wage fairness.
OUR TEAM AND SUCCESS
Quince is a retail and technology company co-founded by a team that has extensive experience in retail, technology and building early stage companies. You’ll work with a team of world-class talent from Stanford GSB, Wish.com, D.E. Shaw, Stitch Fix, Urban Outfitters, Wayfair, McKinsey, Nike etc.
ABOUT THE TEAM:
We are seeking passionate individuals who are enthusiastic about transforming the way people purchase essential goods through innovative data science and AI solutions. We are a centralized data science team that optimize and automate the decision making and provide valuable and actionable business insights. As a Storefront Data Scientist at Quince, you will lead the exploration, design, and implementation of data science solutions to enhance site personalization, ranking, search and discovery. Your work will greatly enhance the customer experience, providing you with the opportunity to significantly influence the future success of Quince.
RESPONSIBILITIES:
- Develop and refine machine learning and statistical models to deliver personalized customer experience, including product search, ranking, and recommendations on both the site and app.
- Create new ranking features and construct end-to-end modeling pipelines utilizing the latest ML/AI technologies.
- Deploy the ML models into production, run experiments, and enable performance monitoring in production.
- Collaborate across functions: Work closely with product, business, and engineering partners to initiate, develop, and deploy solutions with cross-functional support.
- 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.
NEEDED SKILLS:
- MS or PhD in statistics, mathematics, engineering, computer science or another quantitative field AND 5+ years of experience as a data scientist in relevant industry
- Hands-on experience applying machine learning, predictive modeling, GenAI to developing Recommender System, Ranking, and Personalization
- Proficient in Python and SQL
- Excellent communication and presentation skills
- Experience with BI platforms such as Looker, Tableau etc
- Move fast, be a team player, and kind
DEEP KNOWLEDGE IN
- Statistical and machine learning techniques
- Data Science libraries in a programming or scripting language
- Model Productionalization