Data Scientist, Consumables Category Productivity

Job summary

The North American Consumables organization is seeking a Data Scientist to design, evangelize, and implement solutions to address complex, business questions using advanced statistical and Machine Learning (ML) techniques, experimentation, and big data. You will work with Economists, Scholars, Business Intelligence Engineers, Product Managers, and Software Engineers to design, evaluate, interpret, and optimize continuous experimentation as well as to generate actionable insights from the experiment data. You will conduct statistical analyses, develop ML models, and build data analysis tools to help scale novel experimentation and causal inference approaches globally across Amazon’s Consumer business.


This is a unique, high visibility opportunity for someone who wants to innovate, have a business impact, dive-deep into a wide range of businesses and programs in Amazon, and work closely with a diverse set of science, business, and technical stakeholders. We are particularly interested in candidates with a passion for and experience in building statistical and ML models, applying approaches at the forefront of industry and academic research, working with large amounts of data, engaging in healthy debates with fellow scientists, and communicating complex statistical analyses and implications to business leaders in an intuitive and simplified way.


Some of the key responsibilities for this role include:
· Collaborating with our dedicated software team to create production implementations for large-scale data analysis and/or ML models.
· Guiding and establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.
· Working with product managers, software engineers, data engineers, Economists, and other scientists to design, develop, implement, refine, automate, and scale highly innovative statistical and ML models.
· Designing and implementing experimentation frameworks and evaluation methodologies to scale globally for the purpose of measuring the impact of business decisions on short- and long-term customer and financial results.
· Optimizing how we offer selection to improve the customer experience and business results.

Basic Qualifications

· Master’s degree in a highly quantitative field (Machine Learning, AI, Statistics, Mathematics, Computer Science, Operational Research, or equivalent).
· 5+ years of experience working in data science, with hands-on industry experience in predictive modeling and analysis, as an ML engineer or data scientist role, applying various ML and other statistical techniques.
· Proficient with using scripting language (e.g. Python), data querying languages (e.g. SQL), and statistical/mathematical software (e.g. R, Stata, SAS, Matlab).
· 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.).
· Expertise with large-scale, complex dataset processing and knowledge of model deployment.
· In-depth knowledge of causal inference, regression, classification, design and evaluation of experiments.
· Superior verbal and written communication skills, ability to convey rigorous mathematical concepts to technical peers and to non-experts.

Preferred Qualifications

· A PhD degree in a highly quantitative field (Machine Learning, AI, Statistics, Mathematics, Computer Science, Operational Research, or equivalent).
· 8+ years’ experience in a ML or Data Scientist role with a large technology company
· Experience with large-scale data warehousing and BI solutions, including using AWS technologies – Redshift, S3, Glue, Athena, SageMaker, Spectrum, EC2, Data Pipeline and other big data technologies.
· Track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
· Experience collaborating with and influencing product managers, engineering teams, and fellow scientists.



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.