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If you’re someone who likes to imagine new possibilities and build better systems rather than plug-in to outdated ones, Quince is the place for you.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 (Data Scientist III : Supply Chain Operations Research)

We are seeking a Senior Data Scientist (Data Scientist III : 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 drive 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 the Staff Data Scientist, Planning Tools Engineering, and supply chain stakeholders across Bangalore and Palo Alto to translate complex operational challenges into scalable, measurable solutions.

Success in this role means building models that move real business metrics, applying rigorous scientific methodology, and ensuring that your solutions are reliable enough to operate in production. You will also contribute to AI-native approaches to Operations Research and help establish strong scientific and engineering practices across the team.

Responsibilities

Operations Research & Modelling

Own meaningful modelling workstreams across areas such as dynamic warehouse routing, shipping cost optimization, multi-modal transportation, inventory placement, network flow, and PO allocation

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

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

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

Select and tune appropriate solvers and optimization approaches based on the characteristics of each problem

Run rigorous experiments and evaluate models against historical and operational data to ensure results are statistically and operationally meaningful

AI-Native Science

Use 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 methodologies based on measurable outcomes and scientific rigor

Validate AI-generated recommendations and hypotheses before incorporating them into production decision-making

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

Production & Engineering

Build and deploy models into production rather than limiting work to analytical prototypes or reports

Own the monitoring, performance evaluation, and iteration cycle for models after deployment

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

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

Ensure models remain performant and reliable as supply chain networks, business conditions, and operational patterns evolve

Contribute to engineering best practices around reproducibility, testing, monitoring, and production model quality

Cross-Geography Collaboration

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

Ensure modelling approaches and systems developed across geographies integrate effectively and avoid duplicated solutions

Communicate methodology, results, assumptions, and trade-offs clearly through written documentation

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

Business Partnership

Work directly with logistics, warehouse, and supply chain planning stakeholders to understand operational challenges

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

Convert model outputs into recommendations and decisions that operations teams can effectively use

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

Influence business and technical 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 shipping 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 with at least one supply chain Operations Research domain such as warehouse routing, transportation optimization, inventory placement, network optimization, or procurement

Strong ability to take modelling problems from problem formulation through production deployment and iteration

Engineering fluency across areas such as feature pipelines, solver integration, experimentation infrastructure, and model serving

Experience working with real-world operational data and handling noisy, incomplete, or changing datasets

Strong experimentation and model evaluation skills, including backtesting and performance measurement

Experience working with optimization solvers and production-grade data science workflows

Demonstrated experience applyi