Sr. Applied Scientist, Supply Chain Optimization

Amazon UK Services Ltd.; London; On-site; GBP 60,000 - 85,000; Full time

Job summary

A leading e-commerce company in London seeks an experienced Applied Scientist to tackle optimization and forecasting problems. The ideal candidate will have strong programming skills in Java, C++, or Python, and expertise in machine learning applications. This role emphasizes building innovative algorithms to enhance supply chain processes, requiring a Master's degree and a background in applied research.

Qualifications

• Master's degree required.
• Programming experience in relevant languages.
• Experience with deep learning and machine learning.

Responsibilities

• Build robust and scalable optimization algorithms.
• Design algorithms using cloud-based techniques.
• Lead complex analyses and communicate results.

Skills

Programming experience in Java, C++, Python

Experience with neural deep learning methods

Machine learning model development

Research and publications in OR/ML

Education

Master's degree

Tools

R

scikit-learn

TensorFlow

Hadoop

Spark

Job description

Social network you want to login/join with:

Sr. Applied Scientist, Supply Chain Optimization, London

Client: Amazon UK Services Ltd.

Location: London, United Kingdom

Job Category: Other

-

EU work permit required: Yes

Job Reference: 231fbf1ba0f1

Job Views: 16

Posted: 12.08.2025

Expiry Date: 26.09.2025

Job Description:

Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth’s most customer-centric company" makes the customer fulfillment business bigger and more complex with each passing year.

The SC Optimization and Automation team within SCOT organization - Supply Chain Optimization Technology - is looking for an exceptionally talented Scientist to tackle complex and ambiguous optimization and forecasting problems for our WW fulfillment network.

The team owns the optimization of our Supply Chain from our suppliers to our customers. We are also responsible for analyzing the performance of our Supply Chain end-to-end and deploying Operations Research, Machine Learning, Statistics and Econometrics models to improve decision making within our organization, including forecasting, planning and executing our network. We work closely with other Supply Chain Optimization Technology teams, with whom we own the systems and the inputs to plan our networks, the worldwide scientific community, and with our internal WW stakeholders within Supply Chain, Transportation, Store and Finance.

We are looking for an experienced candidate having a well-rounded technical/scientific background, and deep expertise in large-scale non-convex non-linear OR optimization (including stochastic), as well as forecasting (including probabilistic). The candidates should have a history of delivering complex scientific projects end-to-end, and be comfortable developing long-term scientific solutions while ensuring the continuous delivery of incremental model improvements in an ever-changing operational environment.

As an Applied Scientist, you will design, develop and deploy robust and scalable scientific solutions via Operations Research and Machine Learning algorithms, especially in the context of stochastic customer demand and other sources of uncertainty requiring to move past deterministic and linear optimization. You will partner with other tech and science teams, operations, and finance to identify opportunities to improve our processes and drive efficiency in our Fulfillment Center network flows.

This role requires a self-starter aptitude for independent initiative and the ability to influence partner scientific and operational teams to drive innovation in supply chain planning and execution. You are passionate, results-oriented, and inventive, obsessing over the quality of your solutions and their fast, scalable implementation to address and anticipate customer needs.

Key job responsibilities

• Build state-of-the-art, robust, and scalable optimization and forecasting algorithms to drive optimal inventory placement and product flows in non-convex, non-linear, and stochastic settings
• Design and engineer algorithms using Cloud-based software development techniques
• Think multiple steps ahead and develop long-term solutions while delivering incremental improvements
• Prototype quickly, pilot, gather feedback, and iterate
• Operationalize solutions by partnering with internal customers and influencing their roadmaps
• Lead complex analysis and communicate results and recommendations to leadership
• Research, apply, and publish the latest OR/ML techniques from academia and industry

BASIC QUALIFICATIONS

• Master's degree
• Programming experience in Java, C++, Python, or related languages
• Experience with neural deep learning methods and machine learning
• Experience in building machine learning models for business applications
• Experience in applied research

PREFERRED QUALIFICATIONS

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, TensorFlow, numpy, scipy, etc.
• Experience with large-scale distributed systems such as Hadoop, Spark, etc.