Staff Data Scientist (TLM)

THE ROLE

Staff Data Scientist – Supply Chain Operations Research

We are seeking a Staff Data Scientist : Supply Chain Operations Research to join our Supply Chain Planning Science team in Bangalore. In this role, you will lead the development and productionization of advanced Operations Research solutions that power critical supply chain decisions across warehouse routing, transportation, network planning, inventory, and purchase order allocation.

As a founding science leader for the India-based Operations Research team, you will own OR workstreams end-to-end — from problem formulation and methodology selection to production deployment, experimentation, and measurable business impact. You will partner closely with Supply Chain Planning, Engineering, Logistics, Warehouse Operations, and Science teams across Bangalore and Palo Alto to translate complex operational problems into scalable optimization solutions.

Success in this role means building production-grade optimization systems that materially improve supply chain efficiency, establishing strong scientific standards for the OR organization, and raising the technical bar through mentorship, collaboration, and AI-native approaches to Operations Research.

Responsibilities:

Operations Research & Workstream Ownership

Own Operations Research workstreams end-to-end across areas such as dynamic warehouse routing, shipping cost optimization, multi-modal transportation, network flow, inventory planning, and PO allocation

Translate complex supply chain and operational challenges into well-defined optimization problems and measurable objectives

Select and apply appropriate optimization methodologies including Linear Programming (LP), Mixed-Integer Programming (MIP), Constraint Programming (CP), vehicle routing, network flow, metaheuristics, and stochastic optimization

Develop, productionize, and continuously improve optimization models based on real-world operational feedback and measurable business outcomes

Partner with Planning Tools and Engineering teams to define requirements for solver integration, feature pipelines, model serving, evaluation infrastructure, and production systems

Methodology & Scientific Rigor

Establish and maintain high methodological standards for Operations Research across the organization

Evaluate different optimization approaches using rigorous, data-driven experimentation rather than relying on a single modeling methodology

Define experimentation standards including clean data splits, historical backtesting, model validation, and robust evaluation frameworks

Work effectively with sparse, noisy, incomplete, and non-stationary operational data

Challenge assumptions, validate hypotheses rigorously, and be willing to reject approaches that do not demonstrate measurable impact

Ensure optimization models are explainable, maintainable, and appropriate for real-world operational decision-making

AI-Native Science & Innovation

Drive the adoption of AI-native workflows across Operations Research, including LLM-assisted model formulation, agentic problem decomposition, and AI-assisted experiment design

Identify opportunities where AI can accelerate scientific workflows while maintaining rigorous validation and methodological standards

Build and improve AI-augmented experimentation and optimization workflows

Establish practical standards for evaluating AI-generated hypotheses, formulations, and recommendations before they influence production decisions

Technical Leadership & Cross-Geography Collaboration

Set the technical and scientific direction for the India-based Operations Research team

Partner closely with the Palo Alto Staff Data Scientist and Director, Supply Chain Planning Science & Platform to establish a cohesive science roadmap across geographies

Communicate complex methodologies, technical decisions, and roadmaps clearly through written documentation and design discussions

Work effectively across distributed teams and time zones, ensuring technical decisions can be executed without requiring constant real-time guidance

Influence engineering and science architecture decisions related to optimization platforms, data pipelines, model serving, and evaluation infrastructure

Mentorship & Team Building

Mentor Data Scientists and Operations Research practitioners through technical guidance, code reviews, design discussions, and direct coaching

Raise the methodological and engineering bar across the OR team

Help define technical standards, best practices, and expectations for high-quality Operations Research

Contribute to hiring and help build a high-caliber Operations Research organization as the team scales

Business Partnership

Partner with supply chain, logistics, warehouse, and planning stakeholders to understand operational challenges and translate them into optimization problems

Convert complex model outputs into clear, actionable recommendations that business and operations teams can use

Educate stakeholders on model capabilities, limitations, assumptions, and appropriate use cases

Challenge over-fitted or incorrectly defined business problems and ensure scientific rigor is maintained in decision-making

Qualifications

Required:

9–12 years of experience in Operations Research, Data Science, Applied Mathematics, Industrial Engineering, or a related quantitative discipline

Deep expertise in optimization methodologies including LP, MIP, Constraint Programming, Vehicle Routing, Network Flow, Metaheuristics, and Stochastic Optimization

Demonstrated experience owning OR or optimization workstreams end-to-end, from problem formulation through production deployment and measurable business impact

Strong understanding of supply chain optimization problems such as warehouse operations, transportation, inventory, network planning, logistics, or procurement

Strong engineering capabilities to productionize analytical and optimization solutions, including feature pipelines, solver integration, model serving, and experimentation infrastructure

Experience working with real-world operational datasets that may be large, noisy, sparse, or non-stationary

Strong understanding of experimentation, backtesting, model evaluation, and scientific validation

Experience partnering with engineering teams to build and deploy production-grade data science or optimization systems

Demonstrated experience mentoring Data Scientists, Operations Researchers, or other technical professionals

Strong written and verbal communication skills, with the ability to influence technical and business stakeholders

Ability to operate effectively in highly ambiguous, fast-paced environments and independently drive complex problems to completion

Experience using AI/LLM tools to accelerate scientific workflows while maintaining rigorous standards for validation and quality

Advanced degree in Operations Research, Industrial Engineering, Computer Science, Statistics, Applied Mathematics, or a related quantitative field preferred

Preferred:

PhD in Operations Research, Industrial Engineering, Computer Science, Applied Mathematics, Statistics, or a related quantitative discipline

Experience building optimization systems for e-commerce, retail, logistics, supply chain, transportation, or warehouse operations

Experience with commercial or open-source optimization solvers such as Gurobi, CPLEX, OR-Tools, or similar

Experience with large-scale vehicle routing, network optimization, or stochastic optimization problems

Experience building production ML/OR platforms and model-serving infrastructure

Experience working with distributed science or engineering teams across multiple geographies

Strong experience applying AI/GenAI to model formulation, optimization problem decomposition, experimentation, or scientific workflows

Experience building and scaling an Operations Research or Data Science function

Demonstrated track record of publishing research, contributing to open-source projects, or presenting technical work in the OR/Data Science community