Data Scientist, World Wide Design Engineering

- 2+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
- Master's degree in a quantitative field, or Bachelor's degree and 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
- 2+ years of data querying languages (e.g. SQL, Hadoop/Hive) experience
- 2+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Proficiency in data visualizations and dashboarding using AWS QuickSight Or Tableau and in MS Excel (including Pivots, VBA, etc.)
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Knowledge of industrial engineering, warehouse design principles. complex MHE Automation & Robotics used in Warehouse or manufacturing environment.

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $125,500/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Are you a data enthusiast? Are you a creative big thinker who is passionate about using data and optimization tools to direct decision making and solve complex and large-scale challenges? If so, then this position is for you! We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the fulfillment center network of tomorrow.

At Amazon Worldwide Fulfillment Design and Engineering, we are designing the future and if you are in quest of an iterative fast-paced environment, where you can drive innovation through data visualization products, advance analytics and machine learning at scale, this is your opportunity.

In this role, your main focus will be to perform analysis, synthesize information, identify business opportunities, provide project direction, and communicate design and technical requirements within the team and across stakeholder groups. You will learn current processes, build metrics, educate diverse stakeholder groups, assist science groups in initial solution design, and audit new flow implementations. A successful candidate in this position will have a background in communicating across significant differences, prioritizing competing requests, and quantifying decisions made.

Key job responsibilities
• Analyze, model and interpret data for Amazon warehouse operational performance
• Data analysis, modeling, network flow prediction using Excel, Pivot tables, VBA, Tableau, SQL (Amazon Redshift), R, Python
• Predictive analysis using Machine Learning models at scale
• Create effective data analysis reports and communicate with stakeholders
• Innovate and simplify data analysis process to standardize and enable automation
• Support Worldwide Engineering teams by providing necessary data for of process flows and selection of various MHE's
• Manage multiple data analysis projects and tasks simultaneously and effectively influence, negotiate, and communicate with internal and external business partners, contractors and vendors.
• Support process improvement initiatives among site operations, engineering, and corporate systems groups by providing relevant data.
• Develop data and optimization-based solutions to the best-in-class process flow to improve the throughput of the fulfillment facilities.