Principal Applied Scientist, Fulfillment Optimization, Network Planning and Fulfillment Execution S - Amazon

Description Amazon Fulfillment Optimization, Network Planning and Fulfillment Execution Science group is seeking a Principal Applied Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. Network Planning and Execution tackles some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide. We own Amazon's global fulfillment center and transportation topology planning and execution. The team also owns the short-term network planning that determines the optimal flow of customer orders through Amazon fulfillment network. This includes developing sophisticated math models and controllers that assign orders to fulfillment centers to be picked and packed and then planning the optimal ship method in terms of cost, speed and carbon impact to deliver to the customer. These plans drive downstream decisions that are in the billions of dollars. The systems we build are entirely in-house, and are on the cutting edge of both academic and applied research in large scale supply chain planning, optimization, machine learning and statistics. These systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimize Amazon's fulfilment network. As Amazon continues to build and expand the first party delivery network, this role will be critical to realize this vision. Your tech solution will have large impacts to the physical supply chain of Amazon, and play a key role in improving Amazon consumer business's long-term profitability. If you are interested in diving into a multi-discipline, high impact space this is the team for you. Key job responsibilities As a Principal Applied Scientist within Network Planning and Execution team, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised, semi-supervised and unsupervised learning, reinforcement learning, advanced statistical modeling, and graph models. You will have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry, with some of the best product managers, research scientists, statisticians, and software engineers to integrate scientific work into production systems. You will partner with the senior tech leaders in the organization to define the long-term vision of our network planning and execution systems. You will play a key role in developing long term strategic solutions that have business impact beyond the scope of the organization. You will bring deep technical expertise in the area of Machine Learning, and will play an integral part in building Amazon's Fulfillment Optimization systems. Other responsibilities include: - Research and develop machine learning models to solve diverse business problems faced within Network Planning and Execution team. - Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. - Review and audit modeling processes and results for other scientists, both junior and senior. - Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers - You will ensure senior leaders in the organization are up to speed on important trends, tools and technologies and how they will be used to impact the business. A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. As a Principal Applied Scientist on the team, you will be involved in every aspect of the process - from ideation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with bar raising scientists, engineers, and designers. You are expected to make decisions about technology, models and methodology choices. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work and mentor engineers and other scientists. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. A successful candidate is able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Fulfillment Optimization, Network Planning and Fulfillment Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA Basic Qualifications - Ph.D. in Computer Science, Operations Research, Operations Management, Industrial Engineering, Statistics, Applied Mathematics, or a related field - 10+ years of hands-on experience in building machine learning models in business environment - 10+ years of production coding experience in one of the object-oriented programming languages such as Java, Python, C++, etc. - Proven track in leading, mentoring and growing teams of scientists with demonstrated ability to serve as a technical lead - Strong fundamentals in problem solving, algorithm design and complexity analysis Preferred Qualifications - Prior well-established world-class recognized academic experience including multiple publications in top-tier academic journals; or industry experience conducting research in the aforementioned areas with proven track record in developing solutions for major business problems, demonstrating significant innovation, creativity and judgment - Deep technical knowledge of machine learning and statistical methodologies - Experience of applying hybrid techniques in the space of ML and Operations Research is a big plus - Experience with fully automated machine training (e.g., automatic re-training, automatic testing) - Experience in Forecasting and Supply Chains including experience with complex supply chains (>100 facilities, >1000 routes) is a plus - Experience with high-impact decisions (>$1B) - 5+ years modeling supply chain related problems - Excellent communication skills, both written and oral catered appropriately towards both technical and business people. Demonstrated ability to document the models and analysis and present the results/conclusions in order to influence business critical decisions 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 $159,100/year in our lowest geographic market up to $309,400/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. Applicants should apply via our internal or external career site.