Have you ever ordered a product on Amazon and when that box with the smile arrives you wonder how it got to you so fast? Wondered where it came from and how much it would have cost Amazon? If so, Amazon’s Supply Chain Optimization Technologies (SCOT) team is for you. We build systems to peer into the future and estimate the distribution of tens of millions of products every week to Amazon’s warehouses in the most cost-effective way. When customers place orders, our systems use real time, large scale optimization techniques to optimally choose where to ship from and how to consolidate multiple orders so that customers get their shipments on time or faster with the lowest possible transportation costs. This team is focused on saving hundreds of millions of dollars using cutting edge science, machine learning, and scalable distributed software on the Cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing and supply.
To help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scot
A day in the life
As a Sr. Data Scientist, you will solve real world problems by analyzing large amounts of business data, defining new metrics and business cases, designing simulations and experiments, creating ML models, and collaborating with teammates in business, software, and research. The successful candidate will have a strong quantitative background and can thrive in an environment that leverages statistics, machine learning, operations research, econometrics, and business analysis.
About the hiring group
Fulfillment-by-Amazon (FBA) Inventory Technology (FIT) at Amazon’s Supply Chain Optimization Technologies (SCOT) focuses on driving long term free cash flow by automating and optimizing our third-party supply chain. The team’s efforts will address the key challenges facing the worldwide FBA Seller business, including 1) improving FBA Seller inventory efficiency, 2) efficiently balancing the supply and demand of FBA Seller capacity, 3) closing worldwide selection gap by enabling global selling profitability, and 4) driving out costs across the FBA supply chain to spin the flywheel. This is truly a unique problem space – optimizing for inventory in Amazon’s pipeline when you don’t control the process or own the inventory.
· Working with product managers, software engineers, data engineers, other data scientists, applied scientists to design, develop, and evaluate highly innovative statistics and ML models to drive FBA growth and efficiency through inventory optimization and the design of new policies and incentives.
· Guide and establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
· Proactively seek to identify business opportunities and insights and provide solutions to automate and optimize key business processes and policies based on a broad and deep knowledge of Amazon data, industry best-practices, and work done by other teams.
· Collaborating with our dedicated software team to create production implementations for large-scale data analysis and/or ML models.
· Developing and owning key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business
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.
· Master’s degree in a highly quantitative field: Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.
· 4+ years of hands-on industry experience in predictive modeling and analysis, causal inference, or multivariate statistics, as an ML engineer or data scientist role, applying various ML techniques, and deep understanding the key parameters that affect their performance.
· Strong Analytical skills – has ability to scope out business problems to be solved, start from ambiguous problem statements, identify and access relevant data, make appropriate assumptions, perform insightful analysis and draw conclusion relevant to the business problem.
· Proficient with Python and data manipulation/analysis libraries such as Scikit-learn and Pandas for analyzing and modeling data.
· Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.).
· Experience with managing large and disparate data sources
· Excellent communication skills. Proven ability to communicate verbally and in writing to technical peers and business teams, educating them about our systems, as well as sharing insights and data-driven recommendations
· A PhD degree in a highly quantitative field (Machine Learning, AI, Computer Science, Statistics, Mathematics, Operational Research, etc.).
· 8+ years’ experience in a ML or Data Scientist role with a large technology company.
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, , information retrieval.
· Skilled with Java, C++, or other similar programming language.
· Functional knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
· Advanced knowledge and expertise with Data modeling skills, Advanced SQL with Oracle, MySQL, Redshift and Columnar Databases
· Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
· Track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment