Hundreds of millions of customers, billions of transactions, petabytes of data… How to use the world’s richest collection of e-commerce and device usage data to acquire new customers, target existing customers, and predict customer behavior? Amazon’s 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 long-term roadmap for analytic capabilities. This is an opportunity to join a group with a broad charter and stakeholders across Amazon. Amazon.com has a culture of data-driven decision-making and demands business intelligence that is timely, accurate, and actionable. This team provides a fast-paced 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, cross-functional 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 decision-making.
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 end-user facing applications/reports. These solutions will be fault tolerant, self-healing 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 detail-oriented and must have an aptitude for solving unstructured problems. You should work in a self-directed 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 large-scale 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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