Staff Data Scientist (TLM)

ABOUT QUINCE 

Founded in 2018, Quince was built to challenge the idea that nice things have to cost a lot. Our mission is simple: to make really high quality essentials for really low prices, produced fairly and sustainably. We believe everyone deserves exceptional craftsmanship and timeless design without the traditional markups. Quince is a direct-to-consumer (DTC) model that cuts out middlemen and leverages just-in-time manufacturing to minimize waste and maximize value.

Quince is a tech company disrupting the retail industry by putting AI, analytics and automation at the center of everything we do. Our unwavering commitment to excellence and company values guide our teams and actions:

• Customer First: We prioritize customer satisfaction in every decision.

• High Quality: True quality means premium materials and rigorous production standards you can feel good about.

• Essential Design: We focus on timeless, functional essentials instead of chasing trends.

• Always a Better Deal: Innovation and transparency ensure value for both customers and partners.

• Social & Environmental Responsibility: We commit to sustainable materials, ethical production, and fair wages.

Quince partners with world-class manufacturers across the globe and serves millions of customers. With strong investor backing and a focus on sustainable growth, we are a company that is rapidly scaling while maintaining a commitment to quality, simplicity, and radical price transparency.

OUR TEAM AND SUCCESS 

At Quince, you will be part of a high-performing team that is redefining what quality, value, and sustainability mean in modern retail. We are a destination for builders, innovators, and operators to come together and challenge the status quo. Our collective ambition is bold. We are creating an entirely new category and customer experience – one that democratizes luxury and provides high quality products at radically low prices. That mission demands a world-class team committed to excellence. 

If you are motivated by impact, growth, and purpose, you will find a strong sense of belonging at Quince.

THE ROLE

Staff Data Scientist – Supply Chain Operations Research

We are seeking a Staff Data Scientist : Supply Chain Operations Research to join our Supply Chain Planning Science team in Bangalore. In this role, you will lead the development and productionization of advanced Operations Research solutions that power critical supply chain decisions across warehouse routing, transportation, network planning, inventory, and purchase order allocation.

As a founding science leader for the India-based Operations Research team, you will own OR workstreams end-to-end — from problem formulation and methodology selection to production deployment, experimentation, and measurable business impact. You will partner closely with Supply Chain Planning, Engineering, Logistics, Warehouse Operations, and Science teams across Bangalore and Palo Alto to translate complex operational problems into scalable optimization solutions.

Success in this role means building production-grade optimization systems that materially improve supply chain efficiency, establishing strong scientific standards for the OR organization, and raising the technical bar through mentorship, collaboration, and AI-native approaches to Operations Research.

Responsibilities:

Operations Research & Workstream Ownership

• Own Operations Research workstreams end-to-end across areas such as dynamic warehouse routing, shipping cost optimization, multi-modal transportation, network flow, inventory planning, and PO allocation
• Translate complex supply chain and operational challenges into well-defined optimization problems and measurable objectives
• Select and apply appropriate optimization methodologies including Linear Programming (LP), Mixed-Integer Programming (MIP), Constraint Programming (CP), vehicle routing, network flow, metaheuristics, and stochastic optimization
• Develop, productionize, and continuously improve optimization models based on real-world operational feedback and measurable business outcomes
• Partner with Planning Tools and Engineering teams to define requirements for solver integration, feature pipelines, model serving, evaluation infrastructure, and production systems

Methodology & Scientific Rigor

• Establish and maintain high methodological standards for Operations Research across the organization
• Evaluate different optimization approaches using rigorous, data-driven experimentation rather than relying on a single modeling methodology
• Define experimentation standards including clean data splits, historical backtesting, model validation, and robust evaluation frameworks
• Work effectively with sparse, noisy, incomplete, and non-stationary operational data
• Challenge assumptions, validate hypotheses rigorously, and be willing to reject approaches that do not demonstrate measurable impact
• Ensure optimization models are explainable, maintainable, and appropriate for real-world operational decision-making

AI-Native Science & Innovation

• Drive the adoption of AI-native workflows across Operations Research, including LLM-assisted model formulation, agentic problem decomposition, and AI-assisted experiment design
• Identify opportunities where AI can accelerate scientific workflows while maintaining rigorous validation and methodological standards
• Build and improve AI-augmented experimentation and optimization workflows
• Establish practical standards for evaluating AI-generated hypotheses, formulations, and recommendations before they influence production decisions

Technical Leadership & Cross-Geography Collaboration

• Set the technical and scientific direction for the India-based Operations Research team
• Partner closely with the Palo Alto Staff Data Scientist and Director, Supply Chain Planning Science & Platform to establish a cohesive science roadmap across geographies
• Communicate complex methodologies, technical decisions, and roadmaps clearly through written documentation and design discussions
• Work effectively across distributed teams and time zones, ensuring technical decisions can be executed without requiring constant real-time guidance
• Influence engineering and science architecture decisions related to optimization platforms, data pipelines, model serving, and evaluation infrastructure

Mentorship & Team Building

• Mentor Data Scientists and Operations Research practitioners through technical guidance, code reviews, design discussions, and direct coaching
• Raise the methodological and engineering bar across the OR team
• Help define technical standards, best practices, and expectations for high-quality Operations Research
• Contribute to hiring and help build a high-caliber Operations Research organization as the team scales

Business Partnership

• Partner with supply chain, logistics, warehouse, and planning stakeholders to understand operational challenges and translate them into optimization problems
• Convert complex model outputs into clear, actionable recommendations that business and operations teams can use
• Educate stakeholders on model capabilities, limitations, assumptions, and appropriate use cases
• Challenge over-fitted or incorrectly defined business problems and ensure scientific rigor is maintained in decision-making

Qualifications

Required:

• 9–12 years of experience in Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or a related quantitative discipline
• Deep expertise in optimization methodologies including LP, MIP, Constraint Programming, Vehicle Routing, Network Flow, Metaheuristics, and Stochastic Optimization
• Demonstrated experience owning OR or optimization workstreams end-to-end, from problem formulation through production deployment and measurable business impact
• Strong understanding of supply chain optimization problems such as warehouse operations, transportation, inventory, network planning, logistics, or procurement
• Strong engineering capabilities to productionize analytical and optimization solutions, including feature pipelines, solver integration, model serving, and experimentation infrastructure
• Experience working with real-world operational datasets that may be large, noisy, sparse, or non-stationary
• Strong understanding of experimentation, backtesting, model evaluation, and scientific validation
• Experience partnering with engineering teams to build and deploy production-grade data science or optimization systems
• Demonstrated experience mentoring Data Scientists, Operations Researchers, or other technical professionals
• Strong written and verbal communication skills, with the ability to influence technical and business stakeholders
• Ability to operate effectively in highly ambiguous, fast-paced environments and independently drive complex problems to completion
• Experience using AI/LLM tools to accelerate scientific workflows while maintaining rigorous standards for validation and quality
• Advanced degree in Operations Research, Industrial Engineering, Computer Science, Statistics, Applied Mathematics, or a related quantitative field preferred

Preferred:

• PhD in Operations Research, Industrial Engineering, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline
• Experience building optimization systems for e-commerce, retail, logistics, supply chain, transportation, or warehouse operations
• Experience with commercial or open-source optimization solvers such as Gurobi, CPLEX, OR-Tools, or similar
• Experience with large-scale vehicle routing, network optimization, or stochastic optimization problems
• Experience building production ML/OR platforms and model-serving infrastructure
• Experience working with distributed science or engineering teams across multiple geographies
• Strong experience applying AI/GenAI to model formulation, optimization problem decomposition, experimentation, or scientific workflows
• Experience building and scaling an Operations Research or Data Science function
• Demonstrated track record of publishing research, contributing to open-source projects, or presenting technical work in the OR/Data Science community

WHY QUINCE?

Joining Quince means being part of a mission-driven team reshaping retail. You will work alongside talented colleagues, tackle meaningful challenges, and contribute to building a more sustainable, accessible future for customers and partners alike.

EQUAL OPPORTUNITY & HIRING INTEGRITY

Quince provides equal employment opportunities to all employees and applications for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. 

Quince is committed to providing reasonable accommodations to qualified individuals with disabilities. If you need a reasonable accommodation to complete your application or to perform the essential functions of a role at Quince, please let us know by completing this accommodation form. We review all requests individually and will work with you to determine appropriate accommodations on a case-by-case basis.

Employment is contingent upon successful completion of a background check. Quince will conduct background checks in compliance with applicable federal, state, and local laws.

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.