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 Pharmacys fulfillment network. This is a greenfield opportunity to architect ML-driven supply chain systems from the ground up leveraging Amazons 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 patients 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 teams 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 teams 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

- Knowledge of ML NLP Information Retrieval and Analytics
- Masters 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

- 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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