Join Amazon Pharmacy as the founding engineering leader for our Supply Chain technology team in Bangalore. You will build and lead a team of engineers responsible for the systems that determine what medications to buy, where to place inventory, and how to plan capacity across Amazon Pharmacy's fulfillment network. This is a greenfield opportunity to architect ML-driven supply chain systems from the ground up, leveraging Amazon's cloud-native infrastructure, proven supply chain optimization patterns, and operations research best practices at Amazon scale. You will own the full supply chain stack for Amazon Pharmacy: demand forecasting, procurement optimization, inventory placement, resource planning, and Sales & Operations Planning (S&OP). Your systems will directly determine whether a patient's medication is in stock, at the right facility, at the right time. The stakes are high: pharmacy supply chains operate under regulatory constraints, drug expiry windows, and prescription-driven demand signals that make this one of the most technically interesting supply chain problems at Amazon. We are building an AI-native engineering organization. You will operate with a flat structure, leading senior ICs directly, and leveraging AI-augmented development workflows (code generation, automated testing, ML-driven monitoring) to move fast with a lean team. If you are energized by building ML-intensive systems, leading from the front technically, and setting the culture for a high-autonomy engineering team, this is your role. Key job responsibilities A. Engineering Leadership & Team Building • Lead a team of engineers building ML-driven and optimization-based supply chain systems • Hire engineers who can operate at the intersection of software engineering and quantitative methods • Define the technical and science roadmap: identify high-impact modeling opportunities across demand forecasting, procurement, placement, and planning • Set the bar for scientific rigor: reproducibility, offline evaluation, backtesting, and experiment design • Mentor engineers on translating quantitative methods into production-ready systems • Manage the team's portfolio of work, balancing near-term production improvements with longer-term capability building B. Applied Science & Operations Research • Design demand forecasting systems: time series methods, probabilistic forecasting, hierarchical models that handle sparse pharmacy SKU-level demand • Develop optimization models for procurement: cost minimization under lead time uncertainty, expiry constraints, supplier capacity, and regulatory requirements • Design placement and allocation algorithms: multi-facility inventory optimization, safety stock computation, transfer policies • Apply operations research techniques: linear and integer programming, stochastic optimization, dynamic programming, simulation, multi-objective optimization • Develop capacity and resource planning models: labor demand forecasting, throughput optimization, shift planning • Translate scientific methods into engineering designs that your team can build, test, and deploy C. Production & Experimentation • Own the full system lifecycle: development, offline evaluation, online experimentation, deployment, and production monitoring • Design experimentation frameworks for supply chain interventions where traditional A/B testing is difficult (counterfactual evaluation, synthetic controls, switchback experiments) • Build backtesting and simulation infrastructure to evaluate model performance against historical data before deployment • Define APIs, latency requirements, failure modes, and monitoring dashboards for your team's systems • Establish performance metrics and review cadence to ensure systems improve over time and degrade gracefully D. Collaboration & Influence • Partner with peer SDMs across the supply chain org to align on architecture, interfaces, and priorities • Work with product managers to translate business problems into well-defined optimization objectives • Collaborate across time zones with US-based science and product teams on priorities and research direction • Represent the team in technical and science reviews • Influence the broader supply chain engineering roadmap through data-driven insights and scientific recommendations ### Basic qualifications - Knowledge of ML, NLP, Information Retrieval and Analytics - Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field - 5+ years of scientists or machine learning engineers management experience ### Preferred qualifications - Experience building machine learning models or developing algorithms for business application - Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers - PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field - 5+ years of building machine learning models or developing algorithms for business application experience Our inclusive culture empowers Amazonians to deliver the best results for our customers. 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