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 applying AI/LLM tools to scientific workflows such as model formulation, problem decomposition, or experiment design
- Strong written and verbal communication skills, with the ability to explain complex technical concepts to both technical and non-technical stakeholders
- Ability to work independently in ambiguous environments and collaborate effectively across teams and geographies

Preferred:

- Advanced degree in Operations Research, Industrial Engineering, Computer Science, Statistics, Applied Mathematics, or a related quantitative field
- Experience in e-commerce, retail, logistics, supply chain, transportation, or warehouse optimization
- Experience with optimization solvers such as Gurobi, CPLEX, OR-Tools, or similar
- Experience with large-scale vehicle routing and network optimization problems
- Experience building production-grade optimization or decision-support platforms
- Experience working with distributed Data Science or Operations Research teams
- Strong experience using AI/GenAI tools to accelerate scientific experimentation and modelling workflows
- Experience with cloud-based data science and model deployment platforms

What Success Looks Like:

- A production optimization model is shipped early and demonstrates measurable business impact
- Modelling workstreams have transparent roadmaps, evaluation frameworks, and well-defined iteration cycles
- Optimization models remain reliable and performant as the supply chain network evolves
- Operations and planning teams actively use model outputs to make better business decisions
- Scientific methodology is rigorous, reproducible, and clearly documented
- AI is used meaningfully to accelerate modelling and experimentation without compromising scientific quality
- Strong collaboration exists between the Bangalore and Palo Alto Planning Science teams

📌 Senior Data Scientist ( Data Scientist III : Supply Chain Operations Research) (India)
🏢 Quince
📍 India