Sr. Applied Scientist, Supply Chain Optimization

Amazon; London; On-site; GBP 65,000 - 95,000; Full time

Job summary

A leading company seeks an Applied Scientist to enhance their Supply Chain Optimization team in London. This role involves developing advanced algorithms for forecasting and optimization, requiring expertise in Operations Research and Machine Learning. Candidates must hold a Master's degree and possess strong programming abilities. Join a dynamic team dedicated to innovative solutions in the fast-paced world of e-commerce.

Qualifications

• Master's degree required.
• Proficiency in programming languages like Python, Java, or C++.
• Experience with neural deep learning methods and machine learning applications.

Responsibilities

• Build optimization and forecasting algorithms for inventory placement.
• Design algorithms using cloud-based techniques.
• Lead complex analyses and communicate results to leadership.

Skills

Operational Research

Machine Learning

Statistics

Econometrics

Programming

Education

Master's degree

Tools

R

scikit-learn

Spark MLLib

TensorFlow

Hadoop

Spark

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 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, including forecasting, planning, and execution of our network. We collaborate closely with other Supply Chain Optimization Technology teams, the worldwide scientific community, and internal WW stakeholders within Supply Chain, Transportation, Store, and Finance.

We seek an experienced candidate with a strong technical/scientific background and deep expertise in large-scale non-convex non-linear OR optimization (including stochastic) and probabilistic forecasting. The ideal candidate has a history of delivering complex scientific projects end-to-end and can develop long-term scientific solutions while ensuring continuous delivery of incremental improvements in an evolving operational environment.

As an Applied Scientist, you will design, develop, and deploy robust and scalable scientific solutions using Operations Research and Machine Learning algorithms, especially in stochastic demand contexts requiring models beyond deterministic and linear optimization. You will partner with tech, science, operations, and finance teams to identify opportunities for process improvements to enhance our Fulfillment Center network flows.

This role requires a self-starter attitude, the ability to influence partner teams, and a passion for innovative, scalable solutions that address customer needs efficiently.

Key job responsibilities

1. Build state-of-the-art, robust, and scalable optimization and forecasting algorithms for inventory placement and product flows in complex stochastic settings.
2. Design and engineer algorithms using cloud-based software development techniques.
3. Develop long-term solutions with multiple iterations and continuous improvements.
4. Prototype quickly, pilot early, gather feedback, and refine models.
5. Operationalize solutions by partnering with internal customers to understand needs and influence roadmaps.
6. Lead complex analyses, communicate results, and provide recommendations to leadership.
7. Engage in research, apply, and publish the latest OR/ML techniques internally and externally.

Qualifications:

• Master's degree.
• Proficiency in programming languages such as Java, C++, Python, or related.
• Experience with neural deep learning methods and machine learning applications.
• Background in applied research.
• Experience with modeling tools like R, scikit-learn, Spark MLLib, MxNet, TensorFlow, numpy, scipy.
• Experience with large-scale distributed systems such as Hadoop, Spark.

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