Are you interested in applying your strong quantitative analysis and big data skills to world-changing problems? Are you interested in driving the development of methods, models and systems for strategy planning, transportation and fulfillment network? Are you interested to cooperate with Amazonians around the world? If so, then this is the job for you.
Our team is responsible for creating core analytics tech capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes. We standardize and optimize data sources and visualization efforts across geographies, builds up and maintains the online BI services and data mart. You will work with professional data engineers, scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East.
Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools.
Major responsibilities include:
· Translating business questions and concerns into specific analytical questions that can be answered with available data using Statistical and Machine Learning methods.
· Design and develop complex mathematical, simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions in the areas of inventory management, network flow, supply chain optimization, demand planning.
· Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows and algorithms to design optimal or near optimal solution methodologies to be used by in-house decision support tools and software.
· Prototype models by using modeling and programming languages with efficient data querying and modeling infrastructure.
· Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions.
· Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds.
· Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time.
· Master's degree or Ph.D. in Machine Learning, Statistics, Applied Mathematics, Operations Research, Computer Science or a related quantitative field.
· 3+ years of hands-on experience in building, iterating and validating statistical models.
· 2+ years of hands-on experience in Python, Perl, Scala, Java, C#, C++ or other similar languages
· 1+ years of professional experience in software development
· Proficiency in model development, model validation and model implementation for large-scale applications
· Ability to convey mathematical results to non-science stakeholders Strength in clarifying and formalizing complex problems
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
· Ph.D. in Machine Learning, Statistics, Applied Mathematics, Operations Research, Computer Science or a related quantitative field.
· 6+ years of practical experience applying ML to solve complex problems in an applied environment;
· Significant peer-reviewed scientific contributions in premier journals and conferences;
· Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis;
· Experience with defining research and development practices in an applied environment.