Sr. Research Scientist

Job ID: 1639264 | Amazon Dev Centre Canada ULC

DESCRIPTION

Are you an exceptional science leader who is interested in building innovative products that optimize a global supply chain? Within Amazon's Supply Chain Optimization Technology (SCOT) team, the Capacity team owns the systems that help decide where to place hundreds of millions of units of inventory a week, ensuring that we are optimizing capacity across our supply chain globally to get inventory closer to customers. We are looking for a Sr RS that enjoys solving complex supply chain problems and has a deep knowledge of analyzing large-scale data using optimization and machine learning, leading the team to success. As a leader in SCOT, you will own and drive improvements in the Amazon's supply chain, continually raising the bar by delivering Supply Chain efficiencies. There are no textbook solutions to the problems we are solving and very few attempts have been made to solve at Amazon's scale, which necessitates an analytical thinking to solve problems.

The ideal candidate will be a proven sciences leader who is a self-starter comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced environment. You will have excellent business, technical, analytical and data dive-deep skills. You are effectively able to work with product, business and technology leaders to define and prioritize key customer problems, build data acquisition and integration pipelines to create data sets, develop statistical and machine learning models and deliver analyses and insights that answer these problems. You will have strong quantitative modeling skills and expertise using data mining and statistical analyses at web-scale to coach and guide the team to produce actionable insights and recommendations. You will lead by example and are comfortable taking on projects and delivering results as an individual contributor.

BASIC QUALIFICATIONS

· PhD in a quantitative field such as Mathematics, Statistics, Physics, Engineering, Computer Science, Machine Learning, Economics and 5+ years of industry experience OR MS or greater in a quantitative field and 9+ years of experience (after graduation)
· Strong coding and problem-solving skills in at least one programming language such as Python, Java, SQL, etc.
· Familiar with AWS environment, such as S3, Sage Maker and other
· Experience with linear Optimization or Stochastic Optimization model to make big-impact decision making. Familiar with using CPLEX/XPRESS/Gurobi
· Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.
· Superior analytical skills. Demonstrated ability to identify and solve ambiguous problems
· Excellent communication (verbal and written) and collaboration skills that enable you to earn trust at all levels.

PREFERRED QUALIFICATIONS

· Experience with fully automated machine training (e.g. automatic re-training, automatic testing) on techniques such as Random Forest, Regression (Linear), Time-Series (ARIMA) and Neural network (LSTM, CNN) using 1+GB datasets.
· Experience with high-impact decisions (> $50M)
· Experience with forecasting in production, providing bridging decisions and explanation of models and its impact
· Experience with consulting senior management on benefits of new supply chain design based economic ROI analysis using stochastic optimization techniques.
· Experience building optimization model using XPRESS/Mosel, Gurobi or CPLEX. Fine tuning and designing complex mathematical problem into various decomposition algorithm.
· Experience writing production-quality code using collaborative process such as Git and AWS.

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, disability, age, or other legally protected status. If you would like to request an accommodation, please notify your Recruiter.