Staff Data Scientist (TLM)

ABOUT QUINCE

Quince is a destination for builders, creators, innovators, and operators who want to come together and challenge the status quo. Our mission is simple: make really high quality essentials for really low prices, fairly and sustainably. We deliver on that mission through a unique manufacturer-to-consumer (M2C) model eliminating the layers of traditional retail that add cost and result in consumers paying more than they need to. We find, build relationships with, and work directly with the manufacturing partners behind some of the world’s finest products. From there, our teams design smart, efficient operational processes and build and deploy proprietary technology, AI, and analytics to help us scale fast.

What began with a small assortment of elevated basics has quickly grown into a cross-category brand spanning apparel, accessories, home goods, and more. Today, tens of millions of people across a growing number of countries come – and return – to Quince because they trust us to deliver.

OUR CULTURE

Quince is a culture built for builders by builders. Our way of working starts with a blank sheet of paper. We question conventional thinking, use technology and data to uncover new opportunities, and move quickly to turn ideas into reality. We aren’t interested in replicating how others do retail. We’re building a better way – at a speed and scale unlike anything that’s been done before.

We dream big and chase the hard problems others shy away from. Rejecting long-held assumptions is part of our company's DNA. Where conventional wisdom says you have to choose – soft or durable, speed or rigor, quality or price – we ask why that trade-off has to exist in the first place.

Our pace is fast and the bar is high because our customers expect a lot from us and we refuse to let them down. We believe the best results come from challenging ourselves, learning from one another, and building on each other's strengths.

Here, responsibility is not determined by role, tenure, or seniority. Every team member - no matter their level - has the opportunity to drive our business and shape our trajectory.

If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.

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