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

Quince· Logistics Engineering
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Hybrid (Bengaluru)full-timePosted 2mo ago

About the role

Senior Data Scientist – Supply Chain Operations Research in Bengaluru, India. Develops and productionizes advanced Operations Research and optimization solutions for warehouse routing, transportation, inventory planning, and network optimization; end-to-end ownership from problem framing to production deployment and monitoring.

Responsibilities

  • 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.
  • 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.
  • Build and deploy optimization models into production; monitor, evaluate, and iterate post-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 evolve.
  • Apply engineering best practices around reproducibility, testing, monitoring, experimentation, and production model quality.
  • Collaborate with the Palo Alto Planning Science team; ensure cross-site integration and avoid duplication.
  • Communicate modelling approaches, results, limitations, and trade-offs through clear documentation.
  • Collaborate asynchronously across distributed teams and geographies.
  • Work with logistics, warehouse, and supply chain planning stakeholders to translate operational realities into optimization problems and actionable modelling requirements.
  • Convert model outputs into recommendations usable by operations teams; communicate strengths, limitations, and appropriate applications of models.
  • Influence technical and business roadmaps through data-driven insights and rigorous modelling.

Qualifications

  • 5–8 years of experience in Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or related quantitative field.
  • Experience building and deploying production models with 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 (warehouse routing, transportation optimization, inventory placement, network optimization, procurement).
  • Ability to take modelling problems from formulation to production deployment and iteration.
  • Strong engineering fluency across feature pipelines, solver integration, experimentation infrastructure, and model serving.
  • Experience with real-world operational data (noisy, incomplete, or changing datasets).
  • Strong experimentation, backtesting, and model evaluation skills.
  • Experience with optimization solvers and production-grade DS workflows.
  • Demonstrated experience applying AI/LLM tools to scientific workflows (model formulation, problem decomposition, experiment design).
  • Strong written and verbal communication skills; able to explain technical concepts to technical and non-technical stakeholders.
  • Ability to work independently in ambiguous environments and collaborate across teams and geographies.

Skills

Required:Operations ResearchLinear Programming (LP)Mixed-Integer Programming (MIP)Vehicle RoutingNetwork FlowSolver Integration
Nice to have:Constraint Programming (CP)AI/GenAI in modelling

Benefits

  • Not specified in description
Last updated Sep 9, 2026. Interested? Apply on the company site.
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