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- Tasks: Join us as an Applied Scientist to optimise our global supply chain using advanced algorithms.
- Company: Amazon is the world's fastest-growing e-commerce company, dedicated to customer satisfaction.
- Benefits: Enjoy flexible working options, competitive pay, and a vibrant workplace culture.
- Why this job: Be part of a team that drives innovation and efficiency in supply chain management.
- Qualifications: PhD or Master's degree with experience in machine learning and optimisation required.
- Other info: Opportunity to publish research and collaborate with top scientists in the field.
The predicted salary is between 48000 - 84000 ÂŁ per year.
Applied Scientist, Supply Chain Optimization
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 (inc. stochastic), as well as forecasting (inc. probabilistic). The candidates should have an history of delivering complex scientific projects end-to-end, and is comfortable in developing long term scientific solutions while ensuring the continuous delivery of incremental model improvements and results 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, finance to identify opportunities to improve our processes in order to drive efficiency improvements 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 so to drive innovation in supply chain planning and execution. You are passionate, results-oriented, and inventive scientist who obsesses over the quality of your solutions and their fast and 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 optimization settings
– Design and engineer algorithms using Cloud-based state-of-the art software development techniques
– Think multiple steps ahead and develop for long term solutions while continuously delivering incremental improvements to existing ones
– Prototype fast, ensure early adoption via pilots, integrate feedback into the models, and iterate
– Operationalize (i.e. deliver) your science solutions by closely partnering with internal customers, understand their needs/blockers and influence their roadmap
– Lead complex analysis and clearly communicate results and recommendations to leadership
– Act as an active member of the science community by researching, applying and publishing internally/externally the latest OR/ML techniques from both academia and industry
BASIC QUALIFICATIONS
– PhD, or a Master\’s degree and experience applying theoretical models in an applied environment
– Experience in solving business problems through machine learning, data mining and statistical algorithms
– Experience in patents or publications at top-tier peer-reviewed conferences or journals
– Experience programming in Java, C++, Python or related language
– 3+ years experience in commercial OR tools (e.g. CPLEX, Gurobi, XPRESS)
– 3+ years experience in developing OR algorithm for non-convex and non-linear optimization problems
– 2+ years experience with Stochastic Optimization algorithms (e.g. Stochastic Linear Programming, Stochastic Dynamic Programming) and ML for Probabilistic Forecasting
– Sharp analytical abilities, excellent written and verbal communication skills
– Ability to handle ambiguity and fast-paced environment
PREFERRED QUALIFICATIONS
– Experience in professional software development
– Reinforcement Learning
– Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
– Experience diving into data to discover hidden patterns and of conducting error/deviation analysis
– Familiarity with Operations concepts – Planning, Forecasting, Optimization, and Customer experience – gained through work experience or graduate level education
– Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
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 a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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Applied Scientist, Supply Chain Optimization employer: Amazon
Contact Detail:
Amazon Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Scientist, Supply Chain Optimization
✨Tip Number 1
Familiarise yourself with the latest trends in Operations Research and Machine Learning. Being well-versed in current methodologies will not only help you during interviews but also demonstrate your passion for the field.
✨Tip Number 2
Network with professionals in the supply chain optimisation space. Attend relevant conferences or webinars, and connect with people on platforms like LinkedIn to gain insights and potentially get referrals.
✨Tip Number 3
Prepare to discuss specific projects where you've applied non-convex and stochastic optimisation techniques. Be ready to explain your thought process, challenges faced, and how you overcame them.
✨Tip Number 4
Showcase your ability to work collaboratively by preparing examples of how you've partnered with cross-functional teams in the past. Highlight your communication skills and how they contributed to successful project outcomes.
We think you need these skills to ace Applied Scientist, Supply Chain Optimization
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in operations research, machine learning, and statistical algorithms. Emphasise any projects where you've tackled complex optimisation problems or developed forecasting models.
Craft a Strong Cover Letter: In your cover letter, express your passion for supply chain optimisation and how your background aligns with Amazon's mission. Mention specific experiences that demonstrate your ability to handle ambiguity and drive innovation.
Showcase Technical Skills: Clearly list your programming skills (Java, C++, Python) and experience with commercial OR tools. Provide examples of how you've applied these skills in real-world scenarios, particularly in non-convex and stochastic optimisation.
Highlight Communication Abilities: Since the role involves communicating complex analyses to leadership, include examples of how you've effectively communicated technical results in previous roles. This could be through presentations, reports, or publications.
How to prepare for a job interview at Amazon
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with Operations Research and Machine Learning in detail. Highlight specific projects where you've applied these techniques, especially in non-convex and stochastic optimisation settings.
✨Demonstrate Problem-Solving Skills
Expect to tackle complex optimisation problems during the interview. Practice explaining your thought process clearly and logically, as this will showcase your analytical abilities and how you approach ambiguity.
✨Communicate Effectively
Strong communication skills are crucial for this role. Be ready to explain your findings and recommendations succinctly, as well as how you would influence internal stakeholders to adopt your solutions.
✨Familiarise Yourself with AWS Tools
Since familiarity with AWS services is preferred, brush up on your knowledge of tools like S3, Redshift, and SageMaker. Being able to discuss how you've used these tools in past projects can set you apart from other candidates.
