At Amazon.com we are working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. Amazon's Consumables team is looking for an analytical, driven, and creative leader to drive strategically important operational initiatives through business intelligence and leveraging the power of data science, statistical analysis, machine learning and other analytical methods.
The Senior Data Scientist is an innovative, results driven professional that executes continuous improvement through data-driven actions. The individual must exercise strong judgment and autonomy in the identification and execution of strategic improvement plans built on cross-Retail and Operations partnerships. Though the primary focus area is US Consumables, this role will impact and inform how Amazon executes Consumables businesses on a global scale.
Major responsibilities of this role include:
Translate / Interpret
. Complex and interrelated datasets describing customer behavior, messaging, content, product design and financial impact.
. Intake business requirements to define the right specialty solution on whether data science, research science, or business intelligence work will be needed.
Measure / Quantify / Expand
. Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance.
. Analyze historical data to identify trends and support decision making in areas like product discovery solutions, selection identification, and customer contacts.
. Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters.
. Provide requirements to develop analytic capabilities, platforms, and pipelines.
. Apply statistical or machine learning knowledge to specific business problems and data.
Explore / Enlighten
. Formalize assumptions about how users are expected to behave, create statistical definition of the outlier, and develop methods to systematically identify these outliers. Work out why such examples are outliers and define if any actions needed.
. Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes.
. Make Decisions and Recommendations
. Build decision-making models and propose solution for the business problem you defined
. Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication.
. Utilize code (Python/R/SQL) for data analyzing and modeling algorithms
The individual should possess a track record of successful relationship management, strong business and analytical acumen, and experience working with technology and engineering teams. Great communication skills, ability to think big and influence cross Amazon teams and high ownership to deliver successful results will be essential.
. Master's Degree in Statistics, Applied Mathematics, Operation Research, Economics or a related quantitative field.
. 5+ years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, Weka, SAS, Matlab)
. Familiarity with various Data Science disciplines including descriptive analytics, optimization, probability, hypothesis testing, simulation & experimentation, regression, forecasting and machine learning.
. Familiarity with various data visualization tools such as Tableau, Qlikview and QuickSight.
. Able to guide the team to make the right scientific or technical trade-offs to meet long term/short-term business needs.
. PhD Degree in Statistics, Applied Mathematics, Operations Research, Computer Science, Economics, or related field
. 7+ years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), or statistical/mathematical software (e.g. R, Weka, SAS, Matlab)
. Experience articulating business questions and using quantitative techniques to arrive at a solution using available data
. Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
. Depth and breadth in quantitative knowledge. Excellent quantitative modeling, statistical analysis skills and problem-solving skills. Sophisticated user of statistical tools.
. Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data
. Combination of deep technical skills and business savvy enough to interface with all levels and disciplines within our customer's organization