Do you have a passion for diving deep to uncover key insights that drive critical business decisions? If yes, the Customer Behavior Cross Channel Optimization (CBA-XO) team is looking for somebody with your enthusiasm and skills to work as part of the team. We are looking for an innovative data scientist who is curious, driven, and passionate about marketing insights and analytics. If you are looking for a role where you can make a major impact, we want to meet you.
The Customer Behavior Analytics (CBA) organization owns Amazon’s insights pipeline, from data collection to deep analytics. We aspire to be the place where Amazon teams come for answers, a trusted source for data and insights that empower our systems and business leaders to make better decisions. Our outputs shape Amazon product and marketing teams’ decisions and thus how Amazon customers see, use, and value their experience. CBA-XO’s mission is to make Amazon’s marketing the most measurably effective in the world. Our long-term objective is to measure the incremental impact of all Amazon’s marketing investments on consumer perceptions, actions, and sales. This requires measuring Amazon’s marketing comparably and consistently across channels, business teams and countries using a comprehensive approach that integrates all Paid, Owned and Earned marketing activity. As the experts on marketing performance we will lead the Amazon worldwide marketing community by providing critical cross-country insights that can power marketing best practices and tenets globally.
This role will work closely with scientists and engineers to develop and run statistical models to understand customer behavior and how customers respond to Amazon’s marketing. You will collaborate directly with economists to produce modeling solutions, partner with software developers and data engineers to build end-to-end data pipelines and production code, and have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, automate, and scale the analysis, and develop metrics that will enable us to continually delight our customers worldwide.
As a successful data scientist, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities.
· Build models and tools using technical knowledge in machine learning, statistical modeling, probability and decision theory, and other quantitative techniques.
· Understand the business reality behind large sets of data and develop meaningful analytic solutions.
· Innovate by adapting to new modeling techniques and procedures.
· Utilizing code (Python, R, etc.) for analyzing data and building statistical models to solve specific business problems
· Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters
· Collaborate with researchers, software developers, and business leaders to define product requirements and provide analytical support
· Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
· Bachelor's Degree
· 1+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
· Master’s degree or PhD in computer science, statistics, information systems, economics, mathematics or similar
· Practical understanding and hands-on experience with regression modelling (linear and logistic)
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences
· Direct experience with both supervised learning methods (linear and logistic regression, time-series modelling, generalized linear models, decision trees, random forests, support vector machines, etc.) and unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components).
· Direct experience analyzing A/B experiments
· Experience building causal inference models
· Proven ability to convey rigorous technical concepts and considerations to non-experts
· Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment