Applied Scientist, SCOT IPC SimJob ID: Amazon UK Services Ltd.Job summary
This job opportunity requires relocation to UK - opening is available in London or Cambridge.
Are you looking for a challenge? Imagine being part of a team that owns one of the largest supply chain simulation systems in the world to predict inventory flows for the millions of items available on ( ) worldwide. Inventory planning involves many algorithms to buy inventory in the right quantities, at the right frequencies, from the right vendors, and assigning to the best warehouse to fulfill customer demand to optimize long term free cash flow for Amazon. Our system lives at the heart of these algorithms, keeping up with the rapid pace of optimization improvements and simulating how they interact with each other. We simulate what these systems will do for months into the future, predicting inventory flows and key operational and financial metrics across the network. This experimentation platform is critical in understanding labor needs, managing our network capacity, and allowing continued optimizations to the many algorithms we simulate. Imagine enabling Amazon's supply chain systems to make data driven decisions based on simulations of trillions of inventory events per day.
Every time an Amazon customer makes a purchase, a number of systems are involved: these systems help optimize inventory acquisition, enable a number of purchase options, ensure great pricing, store products so they are available for fast delivery, and minimize package frustration. The Supply Chain Optimization Technology (SCOT - ) group develops and manages these systems. We are central to Amazon customers' ability to find what they want and get it when they want it. Within SCOT, the IPC Sim (Inventory Planning and Control Simulation) team is responsible for designing and executing the simulations and experiments that measure the impact of SCOT initiatives, as well as predicting inventory flows for labor planning.
Amazon's Cambridge UK based Simulation team is looking for an experienced and passionate Applied Scientist (AS) to join our fast-paced stimulating environment, to help invent the future of retail with technology.
The Inventory Panning and Control (IPC) Simulation team is part of the Supply Chain Optimization Technologies (SCOT) organization. The charter of the SCOT team is to maximize Amazon's return on our inventory investment in terms of Free Cash Flow, and customer satisfaction. We accomplish this by applying simulation, advanced statistical methods, and empirical analysis to predict and evaluate Amazon's inventory needs. The IPC Sim team builds systems that allow SCOT to answer "what if?" questions about our supply chain, our fulfilment network, and our customers. This puts the IPC Sim team at the nexus of operations, logistics, capacity planning, and our retail business teams. To learn more about Supply Chain Optimization Technologies (SCOT) at Amazon, watch this amazing video: To learn more about the work of the team read this blog post:
As an Applied Scientist you will design and develop new machine learning methods that will form the backbone of the simulation and experimentation systems that drive Amazon's retail business forward. You will have access to large datasets with billions of orders and products to build large-scale machine learning systems. Areas of work in this domain include probabilistic modelling, emulation using Gaussian processes, Bayesian optimization and probabilistic numerics, statistical analysis of computer experiments, adaptive experimental design and causal inference. You will work with Researchers, Data Scientists, Data Engineers, Software Engineers, and Product Managers across multiple teams.
Successful outstanding candidates will bring strong technical and analytical abilities, combined with a passion for delivering results for customers, internal and external. This role requires a high degree of ownership, and a drive, to solve some of the most challenging data and engineering problems in retail.
Amazon is an Equal Opportunity - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.
BASIC QUALIFICATIONS
- Undergraduate degree in computer science, software engineering or undergraduate degree in numerical discipline ( physics, maths, engineering).
- Completed PhD in machine learning or statistics or equivalent experience.
- Ability to communicate scientific results and ideas in writing, as evidenced by papers in top machine learning conferences (NeurIPS, ICML, AISTATS, etc) and/or statistical journals (JASA, Annals of statistics, etc)
- Strong verbal and written communication skills and an ability to work in a team environment
- Proven hands-on experience in predictive modelling and analysis.
- 4+ years of experience using scripting languages ( Python)
PREFERRED QUALIFICATIONS
- Expertise in Bayesian computation, Gaussian processes, kernel methods, surrogate modelling (emulation), optimisation, experimental design.
- Experience in python's machine learning and data science stack (tensorflow/pytorch/mxnet, numpy, pandas, matplotlib, scikit-learn, etc).
- Ability to convey rigorous mathematical concepts and considerations to non-experts.
- Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.
- Strong software development skills.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( ) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need an adjustment during the application and hiring process, including support for the interview or onboarding process, please contact the Applicant-Candidate Accommodation Team (ACAT), Monday through Friday from 7:00 am GMT - 4:00 pm GMT. If calling directly from the United Kingdom, please dial (tel: ). If calling from Ireland, please dial (tel: ).