As a Data Scientist at Amazon, you will play an integral part in the measurement and optimization of marketing activities of the organization. You will have the opportunity to work with a rich marketing dataset together with the marketing managers, building models to drive marketing strategy and growth.
You will have many of the following technical and leadership responsibilities:
· Interact with marketing, science, and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements.
· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization.
· Create prototypes and simulations to test devised solutions.
· Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers.
· Work closely with data engineers to integrate prototypes into production systems.
· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features.
· Excel in evaluating the skill sets of candidates to make successful hiring decisions to meet business needs.
· Contribute to progress of the Amazon and broader research communities by producing publications
· Complex and interrelated datasets describing customer behavior, messaging, content, product design and financial impact.
· 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.
· 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.
· 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.
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.
· Bachelor's Degree
· 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
· MSc/Ph.D. in a relevant field
· 5+ years of hands-on experience in modeling and analysis, and in deploying machine learning / deep learning models in production.
· Experience applying various machine learning techniques and understanding the key parameters that affect their performance
· Experience developing experimental and analytic plans for data modeling processes, use of strong baselines, and the ability to accurately determine cause and effect relationship
· Experience with marketing science and analytics
Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation