Amazon Worldwide Advertising is one of Amazon's fastest growing and most profitable businesses. The Advertising Console Product and Technology team is a group of creative individuals whose vision is to make the Amazon Advertising Console (AAC) the most loved and used tool for all advertisers to market and grow their businesses, brands, and products to a global customer base.
The AAC is a collection of federated applications that combine to form the face and brand of Amazon Advertising. ACPT owns the delivery of the software, processes, and tools that allow teams across Amazon to build, support and enhance applications and features that deliver a cohesive advertising experience to all advertisers worldwide. By using our development kit and reusable components, developers can rapidly build features that integrate seamlessly within the suite of advertiser products. We use survey research, data science, machine learning, experimentation, and predictive modeling to understand advertiser dynamics, drive platform optimization, support evidence-based decision making, and help to develop predictive, intelligent features.
As a Principal Data Scientist, you will be responsible for identifying, scoping, and delivering inference-driven features. You will anticipate data and science-related bottlenecks, provide escalation management, anticipate and make trade-offs, and balance business needs versus scientific and technical constraints. An ability to take large, scientifically complex projects and break them down into manageable hypothesis and experiments to inform, functional specifications, then deliver features in a successful and timely manner is expected. Maturity, high judgment, negotiation skills, ability to influence are essential to success in this role.
Principal Data Scientist Job Responsibilities:
• Partner with business, product, and engineering stakeholders in the development of inference-driven features.
• Work closely with business, product, and engineering stakeholders to deliver inference-driven features.
• Actively identify existing and new features which could benefit from predictive modeling and productionization of predictive models
• Actively identify and resolve strategic issues that may impair the team’s ability to meet strategic, scientific, and technical goals
• Partner with research stakeholders to create, maintain, and prioritize the hypothesis and experimentation backlog.
· 8+ years of experience working in a combination of analytics, data science, machine learning, and software product development.
· Evidence of doing or directing science work with high positive impact on business outcomes
· Experience with problem framing across multiple data science and machine learning subdomains, including forecasting, anomaly detection, forecasting, classification, and regression
· Experience with the end-to-end life cycle of data science and machine learning feature development, including program framing, ETL, EDA, experimentation, feature engineering HPO.
· Experience with identifying and addressing issues related to model tuning and model drift.
· Management experience for working on cross-functional projects
· Experience owning feature definition, roadmap development and prioritization
· Experience identifying current and future product features that can enhance the customer experience by leveraging data science and machine learning
· Experience creating, prioritizing, and owning hypothesis and experiment backlogs
· A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent)
· 10+ years of experience working in data science in a consumer product company
· 4+ years of experience managing data scientists, including intermediate managers
· Skilled with Java, C++, or other programming language, as well as with one scripting language such as R, MATLAB, Python or others
· Professional experience in software development (software design and development life cycle)
· Superior verbal and written communication skills, ability to convey rigorous mathematical and statistical concepts and considerations to non-experts.