Data Science Manager

Hundreds of millions of customers billions of transactions petabytes of data How to use the worlds richest collection of ecommerce and device usage data to acquire new customers target existing customers and predict customer behavior Amazons Consumer Behavior Analytics team seeks a Data Science Manager for building analytical solutions that will address increasingly complex business questions.

We are seeking an exceptionally talented leader to lead one of our Data Science teams and develop a longterm roadmap for analytic capabilities. This is an opportunity to join a group with a broad charter and stakeholders across Amazon. has a culture of datadriven decisionmaking and demands business intelligence that is timely accurate and actionable. This team provides a fastpaced environment where every day brings new challenges and new opportunities.

As a Data Science Manager in the team you will be driving the analytics roadmap and will provide descriptive and predictive solutions to the marketing and product management team through a combination of data mining techniques as well as use statistical and machine learning techniques for segmentation and prediction. You will need to collaborate effectively with internal stakeholders crossfunctional teams to solve problems create operational efficiencies and deliver successfully against high organizational standards. You will have leadership for our team of data scientists and play an integral role in strategic decisionmaking.

Key Responsibilities
Define build and lead a team of Data Scientists
Discover areas of the customer experience that can be automated through machine learning
Demonstrate through technical knowledge on Statistical modeling Probability and Decision theory Operations Research techniques and other quantitative modeling techniques
Understand the business reality behind large sets of data and develop meaningful solutions comprising of analytics as well as marketing management
Work closely with internal stakeholders like the business teams engineering teams and partner teams and align them with respect to your focus area
Innovate by adapting new modeling techniques and procedures
You should be passionate about working with huge data sets and be someone who loves to bring datasets together to answer business questions. You should have deep expertise in creation and management of datasets.
You should have exposure at implementing and operating stable scalable data flow solutions from production systems into enduser facing applications/reports. These solutions will be fault tolerant selfhealing and adaptive.
You will extract huge volumes of data from various sources and message streams and construct complex analyses. You will implement data flow solutions that process data real time on message streams from source systems.
You should be detailoriented and must have an aptitude for solving unstructured problems. You should work in a selfdirected environment own tasks and drive them to completion
You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions and to build data sets that answer those questions. You own customer relationship about data and execute tasks that are manifestations of such ownership like ensuring high data availability low latency documenting data details and transformations and handling user notifications and training
Your teams will work with distributed machine learning and statistical algorithms upon a large Hadoop cluster to harness enormous volumes of online data at scale to serve our customers



5 years of building quantitative solutions as a scientist or science manager experience
2 years of scientists or machine learning engineers management experience
5 years of applying statistical models for largescale application and building automated analytical systems experience
PhD in computer science mathematics statistics machine learning or equivalent quantitative field
Knowledge of Python or R or other scripting language

Experience in a least one area of Machine Learning (NLP Regression Classification Clustering or Anomaly Detection)
Experience with fairness in machine learning and artificial intelligence to detect and remove bias in ML/AI systems

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