Staff Data Scientist, ML - 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

Customer First. Customer satisfaction is our highest priority.

High Quality. True quality is a combination of premium materials and high production standards that everyone can feel good about.

Essential design. We don’t chase trends, and we don’t sell everything.  We’re expert curators that find the very best and bring it to you at the lowest prices.

Always a better deal. Through innovation and real price transparency we want to offer the best deal to both our customers and our factory partners.  

Environmentally and Socially conscious.  We’re committed to sustainable materials and sustainable production methods. That means a cleaner environment and fair wages for factory workers.

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, Google, D.E. Shaw, Stitch Fix, Urban Outfitters, Wayfair, McKinsey, Nike etc.

Ideal Candidate:

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.

Requirements:

• 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.
• Deep knowledge in:
  • Statistical and machine learning techniques.
  • Data Science libraries in a programming or scripting language.
  • Model Productionalization.
• 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.

Security Advisory: Beware of Frauds

At Quince, we're dedicated to recruiting top talent who share our drive for innovation. To safeguard candidates, Quince emphasizes legitimate recruitment practices. Initial communication is primarily via official Quince email addresses and LinkedIn; beware of deviations. Personal data and sensitive information will not be solicited during the application phase. Interviews are conducted via phone, in person, or through the approved platforms Google Meets or Zoom—never via messaging apps or other calling services. Offers are merit-based, communicated verbally, and followed up in writing. If personal information is requested to initiate the hiring process, rest assured it will be through secure and protected means.