Description Do you want to work on a team where you are encouraged to build and have the autonomy to push boundaries? Invention has become second nature at Amazon, and the pace of innovation is only accelerating with breadth of our businesses expanding. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new products, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems. The AIM, Planning team within SCOT comprises of S&OP, Inventory Prediction and Entitlement and Long-Term Capacity and Topology Planning. The team's charter is broad and complex and aimed at optimizing the utilization of fulfillment facilities and resources by accurately predicting demand and inventory efficiency measures while reducing stockouts and excess inventory costs across planning horizons, from short-term (within 13 weeks) to the long-term (13 weeks to 5 years). The team's north star is to be the reliable, single source of truth for inventory units and cube demand at granularities ranging from an FC’s bins to overall network level, and across planning horizons as close as next week to as far out as 3-5 years. To get there, we enhance or re-develop models and mechanisms where existing ones fail to account for structural shifts in supply chains, buying programs, or customer behaviors. We create new systems where science-based recommendations are currently lacking and being replaced by heuristics and offline human goal-seeking approaches. We strive to completely eliminate non-scientific interventions in our forecast guidance and capacity recommendations, and replace them with a system-driven outlook to uncover underlying root causes when departing from SCOT plans and recommendations. We institute authoritative and economics-based framework missing today to drive inventory efficiency measures for Retail buying programs (short/long-lead buys) and FBA plans that solve for capacity constraints in the most economical manner across horizons. This is a unique, high visibility opportunity for a senior science leader someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with product managers, engineers, other scientists and economists. We are a Day 1 team, with a charter to be disruptive through the use of ML and bridge the Science and Engineering gaps that exist today. A day in the life In this pivotal role, you will be a technical leader in operations research or machine learning, with significant scope, impact, and visibility. Your solutions have the potential to drive billions of dollars in impact for Amazon's supply chain globally. As a senior scientist manager on the team, you will engage in every facet of the process—from idea generation, business analysis and scientific research to development and deployment of advanced models—granting you a profound sense of ownership. From day one, you will collaborate with experienced scientists, engineers, and product managers who are passionate about their work. Moreover, you will collaborate with Amazon's broader decision and research science community, enriching your perspective and mentoring fellow engineers and scientists. The successful candidate will have the strong expertise in applying operations research methodologies to address a wide variety of supply chain problems. You will strive for simplicity, demonstrate judgment backed by mathematical rigor, as you continually seek opportunities to innovate, build, and deliver. Entrepreneurial spirit, adaptability to diverse roles, and agility in a fast-paced, high-energy, highly collaborative environment are essential. Basic Qualifications - - Ph.D. in Operations Research, Machine Learning, Statistics, Computer Science, Applied Mathematics, Operations Management, Industrial Engineering, Economics or a related field. - - 7+ years of experience in solving complex problems in the area of operations research, statistics or machine learning, as well as in developing strategies for large-scale supply chain, transportation and logistic networks. - - Ability to perform quantitative, economic, and numerical analyses of large-scale systems under uncertainty using statistical and optimization tools such as Python, R and XPRESS to determine solution strategies for complex decision-making problems. - - Fluency in using mathematical optimization or machine learning, including algorithm design, continuous and discrete optimization, stochastic analysis, supervised and unsupervised machine learning methods, and deep learning, to develop solution methods to be used by in-house decision support tools and software. - - Proficiency working with Python, SQL and/or C/C++. - - Excellent written and verbal communication skills while addressing both technical and business people, ability to speak at a level appropriate for the audience, and skills to present business cases, and document models, analyses, and results to influence important decisions. Preferred Qualifications - - 10+ years of hands-on experience in using models to solve complex real-world business problems. - - Experience leading a team of scientists across disciplines including RS, AS, DS - - Experience in supply chains, transportation and logistics models. - - Significant peer-reviewed scientific contributions in premier journals and conferences. - - Programming experience using at least one modern programming language such as Python, Java, or C++. - - Proficiency in development, validation and implementation optimization or machine learning models for large-scale applications. - - Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts. - - Proven ability to work effectively in a cross-functional team. 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 $187,500/year in our lowest geographic market up to $324,100/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.