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

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

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

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

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

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

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

A production optimization model is shipped early and demonstrates measurable business impact

Modelling workstreams have clear 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