Manager, Applied Science, Amazon Phamarcy

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

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