Sr Data Scientist

Seattle, Washington, USA

Full Time Senior-level / Expert
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Would you like to join a fast-paced team committed to creating cutting edge data science tools used to enhance the lives of Amazon Prime members worldwide? Would you like to advance the frontier of using artificial intelligence to make business decisions? If so, Amazon Prime is looking for you! We are seeking a talented data scientist to join the Prime Machine Learning and Economics team, which is the core science team within Amazon Prime. We build recommendation systems, counterfactual-simulation technology, and insight-driving data science to ensure Prime remains one of the world’s most loved membership programs.

A day in the life
A person in this role will work closely with a team of applied scientists, economists, data engineers, software engineers, business intelligence engineers, and product managers to help extract insights from data to inform customer-level (and company-wide) investments and interventions intended to make Prime better. This person will apply state-of-the-art approaches to causal and counterfactual statistical modeling, serving as the front line of statistically analyzing and visualizing our data and bringing its insights to life. These insights will be packaged into strategic insight documents guiding senior company leadership, or used to power Amazon's customer-facing systems (e.g. product search engines).

About the hiring group
Our team is unique in its focus on extracting insights not only from actual customer behavior, but from counterfactual, simulated behavior produced by a series of cutting edge counterfactual simulation statistical tools. These simulation tools operate firmly at “Rung 3” (Counterfactuals) of the Causal Ladder (as defined by Judea Pearl), and are created using newly developed statistical solutions drawing upon traditions in machine learning and econometrics. Another unique aspect of the role is its exposure to both senior-level business professionals and company-wide strategic decision-making processes. These insights matter and drive high-value Amazon investments for our customers, world-wide.

Job responsibilities
· manage a team of scientists and business intelligence engineers to extract insights from raw and simulated counterfactual customer data
· partner with business leaders to package these insights into high quality strategic insight documents and evergreen visualizations and dashboards
· manage the creation of scientific data pipelines useful for building scientific model prototypes at scale
· create code, or manage a team of professionals creating code in scalable distributed computing software to process data (e.g. Spark-Scala, Spark-SQL, PySpark)
· create, or manage the creation of, early stage statistical models using software like Python


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.

Basic Qualifications


· Master’s degree (or Bachelor's degree + 8 years of experience) in a quantitative discipline such as Statistics, Mathematics, Data Science, Business Analytics, Economics, Finance, Engineering, or Computer Science
· 5+ years of experience working as a data scientist or a similar role involving data extraction, analysis, statistical modeling, and communication
· 5+ years of experience using data querying languages (e.g. SQL), scripting languages e.g. Python, or statistical/mathematical software (e.g. R, SAS, Matlab, etc.)

Preferred Qualifications

· A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
· 10+ years of experience working in data science in a consumer product company
· 5+ years of experience managing data professionals (data engineers or business intelligence engineers), machine learning scientists, data scientists, research scientists, applied scientists, and/or economists
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics
· Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
· Ability to manage and quantify improvement in customer experience or value for the business resulting from research outcomes

Job region(s): North America
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