Amazon Data Scientist Builder Insights 

United States, New York, New York 

11729781

You will work with engineers to onboard data, perform scientifically rigorous insight discovery and research, and develop the right statistical and machine learning model-based solutions to deliver impact for builders. You will solve ambiguous problems, working with stakeholders to determine the right questions to ask and developing the roadmap for delivering results. You will apply analytical abilities, business understanding, and technical expertise to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. You will synthesize and communicate insights and recommendations to audiences of varying levels of technical sophistication to influence investment into builder experience improvements through data-backed solutions.Key job responsibilities
• Lead projects, requiring both business and technical domain knowledge, and drive solutions to complex or ambiguous problems with limited guidance.
• Create models and analyses that affect important business decisions.
• Review analyses of other scientists and provide feedback.
• Employ scientifically rigorous standards across multiple data science disciplines, knowing what trade-offs to make to deliver results.
• Build models using Python, R, etc. (and ability to access data using Spark, SQL, or similar)
• Proficiently use at least one common data science language to develop models (Python, R, etc.) and process data (Spark, SQL, etc.).
• Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations

- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment