Amazon
Senior Applied Scientist, Fulfillment Planning & Execution Science - Fulfilment
Amazon, Washington, District of Columbia, us, 20022
Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! At Amazon's Supply Chain Optimization Technologies (SCOT), we are tasked with optimizing the fulfilment on customer orders so that we fulfil all orders worldwide in the most intelligent manner while ensuring Amazon customers get their orders on time.
Amazon Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfilment Optimization group is seeking a Senior Applied Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfilment center and transportation planning and execution. The team also owns the short-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfilment network. This includes developing sophisticated math models and controllers that assign orders to fulfilment 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 at Amazon Scale worldwide. 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. We’re looking for a passionate, results-oriented, and inventive Senior Applied Scientist who can create and improve ML models for our outbound transportation planning systems. In addition, you will be working on design, development and evaluation of highly innovative OR and ML models for solving complex business problems in the area of outbound transportation planning systems.
Key job responsibilities As a Senior Applied Scientist within FPX Science 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 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: Design, development and evaluation of highly innovative ML models for solving complex business problems. Research and apply the latest ML techniques and best practices from both academia and industry. Think about customers and how to improve the customer delivery experience. Use and analytical techniques to create scalable solutions for business problems. Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. Technically lead and mentor other scientists in team. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within its logistics network including Fulfillment Centers, Air Stations, Sort Centers, Delivery Stations, and more. You will need to meet with many internal customers to understand their business and their challenges as you develop an effective solution to enable us to collectively scale. Our leadership is very interested in this effort so you will find lots of opportunities to get your ideas and plans in front of them for evaluation and alignment. 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 Planning & 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 critical 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 and execution 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.
Minimum Qualifications: PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field 5+ years of building machine learning models for business application experience 5+ years of industry or academic research experience Experience with neural deep learning methods and machine learning Experience programming in Java, C++, Python or related language Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Proven track in leading and mentoring scientists with demonstrated ability to serve as a technical lead. Demonstrated ability to document the models and analysis and present the results/conclusions in order to influence business critical decisions. Strong fundamentals in problem solving, algorithm design and complexity analysis. Excellent communication skills, both written and oral catered appropriately towards both technical and business people. Deep technical knowledge of machine learning and statistical methodologies. Prior experience modeling supply chain related problems. 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 with high-impact decisions (>$1B).
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. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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Amazon Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfilment Optimization group is seeking a Senior Applied Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfilment center and transportation planning and execution. The team also owns the short-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfilment network. This includes developing sophisticated math models and controllers that assign orders to fulfilment 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 at Amazon Scale worldwide. 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. We’re looking for a passionate, results-oriented, and inventive Senior Applied Scientist who can create and improve ML models for our outbound transportation planning systems. In addition, you will be working on design, development and evaluation of highly innovative OR and ML models for solving complex business problems in the area of outbound transportation planning systems.
Key job responsibilities As a Senior Applied Scientist within FPX Science 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 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: Design, development and evaluation of highly innovative ML models for solving complex business problems. Research and apply the latest ML techniques and best practices from both academia and industry. Think about customers and how to improve the customer delivery experience. Use and analytical techniques to create scalable solutions for business problems. Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. Technically lead and mentor other scientists in team. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within its logistics network including Fulfillment Centers, Air Stations, Sort Centers, Delivery Stations, and more. You will need to meet with many internal customers to understand their business and their challenges as you develop an effective solution to enable us to collectively scale. Our leadership is very interested in this effort so you will find lots of opportunities to get your ideas and plans in front of them for evaluation and alignment. 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 Planning & 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 critical 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 and execution 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.
Minimum Qualifications: PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field 5+ years of building machine learning models for business application experience 5+ years of industry or academic research experience Experience with neural deep learning methods and machine learning Experience programming in Java, C++, Python or related language Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Proven track in leading and mentoring scientists with demonstrated ability to serve as a technical lead. Demonstrated ability to document the models and analysis and present the results/conclusions in order to influence business critical decisions. Strong fundamentals in problem solving, algorithm design and complexity analysis. Excellent communication skills, both written and oral catered appropriately towards both technical and business people. Deep technical knowledge of machine learning and statistical methodologies. Prior experience modeling supply chain related problems. 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 with high-impact decisions (>$1B).
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. This position will remain posted until filled. Applicants should apply via our internal or external career site.
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