How to use the world’s richest collection of e-commerce data to improve payments experience for our customers? Amazon Consumer Payments Global Data Science team seeks a Sr. Data Scientist for building analytical solutions that will address increasingly complex business questions in the North America Credit space.
Amazon.com has a culture of data-driven decision-making and demands insights that are timely, accurate, and actionable. This team provides a fast-paced environment where every day brings new challenges and new opportunities.
As a Sr. Data Scientist in this team, you will be driving the analytics roadmap and will provide descriptive and predictive solutions to the North America Credit business 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.
· Demonstrate thorough technical knowledge on feature engineering of massive datasets, effective exploratory data analysis, and model building using industry standard regression and classification techniques such as Random Forest, XGBoost package, Keras framework
· 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
· 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 experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· 4+ years working as a Data Scientist
· Masters with 8+ years of experience or Bachelors with 10+ years of experience
· Experience in Python, R or another scripting language; command line / notebook usage. Additional knowledge of SQL strongly preferred
· Track record of diving into data to discover hidden patterns and of conducting error/deviation analysis
· Knowledge of various machine learning techniques and key parameters that affect their performance
· Evidence of using of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. in data analysis projects
· Excellent written and verbal communication skills for both technical and non-technical audiences
· Previous experience in a ML or data scientist role with a large technology company
· Previous experience of building and implementing scalable ML solutions that added value to business
· Experience in creating powerful data driven visualizations to describe your ML modeling results to stakeholders