Senior Data Scientist, Tech Deployment Modeling

Amazon is building the next generation software, hardware, and processes that will run the global network of fulfillment, ground, and air centers that move many millions of units of inventory, and ensure that customers get what they want when we promised.

We are constantly innovating in the ways we are automating and accelerating the planning and delivery of products and packages.

Not only does this involve custom hardware, robotics, supply chain optimization, machine learning, etc. but now also includes modeling, data analysis, and project management optimization in how we deploy technology into our fulfillment centers.

Our team is passionate about solving the toughest industrial engineering challenges in the at scale deployment of advanced technology in Amazon’s global network.

We are seeking a highly skilled and experienced Senior Data Scientist, with a strong background in Industrial Engineering to join our team.

You will play a crucial role in developing advanced analytical models and solutions to drive excellence in field execution as Amazon prepares for robotic deployments at scale.

You will collaborate closely with our team of engineers, data scientists, process experts, and operations personnel to identify opportunities for optimization, process improvements, and efficiency gains.

You will develop and implement data-driven solutions, leveraging cutting-edge techniques in simulation and optimization.

You will be a modeler and a builder, working with customers across Amazon Robotics global organization.

Key job responsibilities

• Design and implement sophisticated analytical models, including queuing models (e.g., M / M / 1, M / G / 1, G / G / c) to analyze and optimize deployment operations.
• Lead the development of deterministic optimization models, such as linear programming (LP) and mixed-integer programming (MIP), to optimize resource allocation, scheduling, and planning
• Develop stock and flow models to simulate inventory management, supply chain dynamics, and resource allocation.
• Build network models (e.g., transportation models, assignment models, and network flow models) to optimize routing, scheduling, and resource allocation in logistics and distribution networks.
• Construct financial models (e.g., Monte Carlo simulations, option pricing models, and portfolio optimization models) to support decision-making in investment planning, risk management, and resource allocation.
• Implement and integrate developed models into existing systems and workflows to streamline processes, resource allocation, and improve operational efficiency.
• Leverage statistical techniques, machine learning algorithms, and optimization methods to extract insights (patterns, trends, correlations) from large, structured and unstructured datasets.
• Collaborate with engineering teams to validate model assumptions, calibrate models based on real-world performance, and refine models to account for dynamic industrial conditions.
• Participate in the ideation and development of new data science initiatives, contributing to the organization's strategic goals in optimizing industrial processes.

BASIC QUALIFICATIONS

• 5+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical / mathematical software (e.

g. R, SAS, Matlab, etc.) experience

• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Bachelor's degree and 8+ years of professional or military experience
• Experience with statistical models e.g. multinomial logistic regression
• Experience in modeling and simulating complex systems that involve interdependencies, feedback loops, and dynamic interactions between various components.

PREFERRED QUALIFICATIONS

• Master's degree in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field
• Experience as a leader and mentor on a data science team
• Experience working with scientists, economists, software developers, or product managers
30+ days ago