Senior Data Scientist ( Data Scientist III : Supply Chain Operations Research)

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If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.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

Senior Data Scientist – Supply Chain Operations Research

We are seeking a Senior Data Scientist – Supply Chain Operations Research to join our Supply Chain Planning Science team in Bangalore. In this role, you will develop and productionize advanced Operations Research and optimization solutions that influence critical supply chain decisions across warehouse routing, transportation, inventory planning, and network optimization.

You will own meaningful modelling workstreams end-to-end, from problem formulation and experimentation through production deployment, monitoring, and iteration. You will work closely with Data Science, Planning Tools Engineering, and supply chain stakeholders across Bangalore and Palo Alto to translate complex operational challenges into scalable, measurable solutions.

This role is ideal for someone who combines strong Operations Research fundamentals with engineering fluency and a bias toward production impact. You will apply rigorous scientific methodology to real-world supply chain problems while helping the team adopt AI-native approaches to modelling, experimentation, and optimization.

Responsibilities

Operations Research & Modelling

Own meaningful modelling workstreams across dynamic warehouse routing, shipping cost optimization, multimodal transportation, inventory placement, network flow, and PO allocation.

Take modelling problems end-to-end, from problem framing and data preparation through model development, deployment, and iteration.

Translate complex operational challenges into well-defined optimization problems with clear objectives, constraints, and measurable success criteria.

Apply Operations Research methodologies including Linear Programming (LP), Mixed-Integer Programming (MIP), Constraint Programming (CP), heuristics, vehicle routing, and network flow.

Select, configure, and tune appropriate optimization solvers and methodologies based on problem characteristics.

Design and run rigorous experiments using historical and operational data to evaluate model quality and business impact.

Analyze model performance, identify limitations, and iterate on approaches based on scientific and operational evidence.

AI-Native Science

Apply AI-native workflows including LLM-assisted model formulation, agentic decomposition of complex optimization problems, and AI-augmented experiment design.

Evaluate AI-generated approaches alongside classical optimization techniques using measurable outcomes and scientific rigor.

Validate AI-generated hypotheses, recommendations, and modelling approaches before incorporating them into production decision-making.

Contribute to team standards and best practices for applying AI effectively and responsibly within Operations Research.

Production & Engineering

Build and deploy optimization models into production rather than limiting work to analytical prototypes.

Own the monitoring, performance evaluation, and iteration lifecycle of models after deployment.

Develop and maintain feature pipelines, optimization workflows, and model-serving components.

Partner with Planning Tools Engineering on solver integration, feature stores, evaluation frameworks, and model-serving infrastructure.

Ensure models remain performant, reliable, and maintainable as supply chain networks and operational conditions evolve.

Apply engineering best practices around reproducibility, testing, monitoring, experimentation, and production model quality.

Cross-Geography Collaboration

Partner with the Palo Alto Planning Science team on shared supply chain optimization problems and modelling methodologies.

Ensure solutions developed across Bangalore and Palo Alto integrate effectively and avoid duplicated efforts.

Communicate modelling approaches, assumptions, results, limitations, and trade-offs through clear written documentation.

Collaborate effectively across distributed teams and time zones with a strong emphasis on asynchronous communication.

Business Partnership

Work closely with logistics, warehouse, and supply chain planning stakeholders to understand operational challenges and business requirements.

Translate operational realities into well-defined optimization problems and actionable modelling requirements.

Convert model outputs into recommendations and decision-support capabilities that operations teams can effectively use.

Clearly communicate the strengths, limitations, assumptions, and appropriate applications of scientific models.

Influence technical and business roadmaps through data-driven insights and rigorous modelling.

Qualifications

Required:

5–8 years of experience in Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or a related quantitative field.

Demonstrated experience building and deploying production models that have delivered measurable business impact.

Strong expertise in optimization methodologies including LP, MIP, CP, heuristics, vehicle routing, and network flow.

Hands-on experience solving supply chain Operations Research problems such as warehouse routing, transportation optimization, inventory placement, network optimization, or procurement.

Ability to take modelling problems from formulation and experimentation through production deployment and iteration.

Strong engineering fluency across areas such as feature pipelines, solver integration, experimentation infrastructure, and model serving.

Experience working with real-world operational data, including noisy, incomplete, or continuously changing datasets.

Strong experimentation, backtesting, and model evaluation skills.

Experience