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 EU SC Science Optimization team is looking for an exceptionally talented Scientist to tackle complex and ambiguous optimization and forecasting problems for our EU/NA 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 Supply Chain Optimization Technology (SCOT) teams, who own the systems and the inputs we rely on to plan our networks, the worldwide scientific community, and with our internal EU stakeholders within Supply Chain, Transportation, Store and Finance.
We are looking for an experienced candidate having a well-rounded-technical/science background, with a particular expertise in stochastic optimization and probabilistic forecasting, as well as a 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 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 Stochastic Optimization and Probabilistic Forecasting algorithms to drive optimal planning under uncertainty and execution in Amazon end-to-end supply chain
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
- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- 3+ years of building models for business application experience
- 2+ years experience with Stochastic Optimization techniques (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
- 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.
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in commercial OR tools (e.g. CPLEX, Gurobi, XPRESS)
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Reinforcement Learning
- Time-series Forecasting
- Deep Learning
- Familiarity with Operations concepts - Planning, Forecasting, Optimization, and Customer experience - gained through work experience or graduate level education
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
